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ModelBuilder Tool produces different results when using Run Button and running as a tool

ModelBuilder Tool produces different results when using Run Button and running as a tool


I found out that I can run a tool I created in ModelBuilder by clicking the run button after clicking to edit the tool. However, it doesn't produce the same results if I run the tool after clicking on it using the ArcCatalog window.

This doesn't work:

But this does:

Correct Output:

Incorrect Output:

Does anyone know why this may be happening?


I made the output feature class of the buffer portion of my tool (CSRS_ORN_NER_Buffer%n%) a model parameter, and even though when I run it it says theres a datum conflict, it seems to work fine through both the ArcCatalog window and ModelBuilder application.


How many times have you walked up to a system in your office and needed to click through several diagnostic windows to remind yourself of important aspects of its configuration, such as its name, IP address, or operating system version? If you manage multiple computers you probably need BGInfo. It automatically displays relevant information about a Windows computer on the desktop's background, such as the computer name, IP address, service pack version, and more. You can edit any field as well as the font and background colors, and can place it in your startup folder so that it runs every boot, or even configure it to display as the background for the logon screen.

Because BGInfo simply writes a new desktop bitmap and exits, you don't have to worry about it consuming system resources or interfering with other applications.

Sysinternals BgInfo


check out SCons. For example Doom 3 and Blender make uses of it.

I have a lot of friends who swear by CMake for cross-platform development:

It's the build system used for VTK (among other things), which is a C++ library with cross-platform Python, Tcl, and Java bindings. I think it's probably the least complicated thing you'll find with that many capabilities.

You could always try the standard autotools. Automake files are pretty easy to put together if you're only running on Unix and if you stick to C/C++. Integration is more complicated, and autotools is far from the simplest system ever.

doit is a python tool. It is based in the concepts of build-tools but more generic.

  • you can define how a task/rule is up-to-date (not just checking timestamps, target files are not required)
  • dependencies can be calculated dynamically by other tasks
  • task's actions can be python functions or shell commands

Some of the GNOME projects have been migrating to waf.

It's Python-based, like Scons, but also standalone -- so rather than require other developers to have your favorite build tool installed, you just copy the standalone build script into your project.

I recommend using Rake. It's the easiest tool I've found.

Other good tools I've used, though, if Ruby's not your thing, are:

Be aware of the ninja build tool (v1.8.2 Sept 2017) which is influenced by tup and redo .

The build file generator cmake (e.g. for Unix Makefiles, Visual Studio, XCode, Eclipse CDT, . ) can also generate ninja build files since version 2.8.8 (April 2012) and, afaik, ninja is now even the default build tool used by cmake .

It is supposed to outperform the make tool (better dependency tracking and is also parallelized).

cmake is an already well-established tool. You can always choose later the build tool without modifying your configuration files. So if a better build is developed in the future which will be supported by cmake you can switch to it conveniently.

Note that for c/c++ improving compilation time is sometimes limited because of headers included through the preprocessor (in particular when using header-only libs, for instance boost & eigen) which hopefully will be replaced by the proposal of modules (in a technical review of c++11 or eventually in c++1y). Check out this presentation for details on this issue.


GIS-SWIAS: Tool to Summarize Seawater Intrusion Status and Vulnerability at Aquifer Scale

In this paper, we introduce GIS-SWIAS, a novel generalized ArcGIS ArcToolbox that helps to analyze seawater intrusion (SWI) status and vulnerability at aquifer scale (SWIAS). It is a user-friendly tool that can be applied to any aquifer and is fully integrated in the ArcGIS environment, which is a widely available software tool. It is the first ArcGIS tool with these characteristics focusing on SWI analyses that we can find in the literature. GIS-SWIAS is able to deal with georeferenced information it is easy to introduce the required data (inputs) and to efficiently perform the demanding computational operations required. Its outputs are in the form of shapes, reports, and images (maps, conceptual cross sections, and time series of lumped indices) to summarize the magnitude, intensity, and temporal evolution of SWI within an aquifer for specific dates or by showing statistics for a chosen time period. It can be applied to assess historical SWI dynamic in cases where there is no groundwater flow model. In those cases, the spatial distribution is assessed by applying simple interpolation techniques. Nevertheless, if we want a rational quantitative analysis of the sustainability of alternative management scenarios to the SWI problem, the GIS-SWIAS tool requires that information on hydraulic head and chloride concentration distribution is generated from simulations of their impacts by a calibrated density-dependent flow model. In such cases, adaptation strategies to potential future scenarios—whose distributed impacts have to be propagated within the previously calibrated models—could usefully be analyzed and compared using this tool. Given all these ways that the GIS-SWIAS tool can be applied, it provides a valuable tool for both the researcher and technician to assess SWI dynamics and aquifer resilience under different scenarios. It can support the decision-making process by helping to make a rational selection of sustainable management strategies. Its performance for the analyses of historical and potential future scenarios has been tested and confirmed in two case studies described in previous research works.

1. Introduction

Seawater intrusion (SWI) in coastal aquifers is a worldwide problem affecting groundwater-dependent ecosystems and human health. In recent decades, society’s awareness of this issue has grown and this has been reflected in the legal framework of many countries. For example, the European Union’s Water Framework Directive (WFD) requires that river basin plans address the achievement of a good qualitative and quantitative status of groundwater bodies [1]. In coastal groundwater bodies, intrusion is one of the main issues that need to be considered to achieve or maintain good groundwater status.

The impacts of SWI on groundwater have a heterogeneous distribution. Analyses of spatiotemporal distribution of SWI require salinity or chloride concentration to be mapped for different dates. Depending on the issue to be addressed and the available information, SWI can be approximated using various models. SWI can be mapped either by applying simple interpolation models [2, 3] to existing point data or by simulating the physical processes using transient density-dependent groundwater flow models [4, 5]. The results obtained with these physical process models can be applied to assess sustainable management strategies, i.e., strategies that prevent deterioration of the aquifer resource due to SWI [6]. They can even be employed to propagate impacts of potential local climate change (CC) [7] or global change (GC) scenarios and to identify adaptation strategies [8].

Based on the salinity or chloride concentration maps at different dates, some authors have defined indices to summarize SWI [9–12]. These indices provide an overview of the intensity and spatial distribution or percentage SWI at aquifer scale. Such indices need to offer descriptive and synthetic results so that the status of SWI in different aquifers and over different periods can be compared. These index-methods [9, 13] establish threshold values for chloride to define the basis of SWI that have been defined in various ways: by referencing natural background levels and/or by taking into account the concentrations required to protect dependent ecosystems or human health [14].

When simple interpolation is used to draw the maps used to define the indices, analyses must be limited to the historical period for which there are data. In contrast, physical process models can be applied to propagate various potential conditions and so maps can be obtained for different scenarios (e.g., alternative management scenarios or future potential CC scenarios) in this latter case, the output can be used to determine the optimum strategy and therefore support the decision-making process [15].

In addition to the SWI status and dynamics, another important issue to take into account in identifying sustainable management strategies for coastal aquifers is the aquifer’s vulnerability to SWI. In the literature we found various methods for mapping SWI vulnerability, such as the GALDIT method [16]. Then, by applying a method to express vulnerability as an index, we can also get an overview of the intensity and spatial distribution or percentage of SWI at aquifer scale [13].

2. Related Works

In the literature we find many tools to assess and analyze water resource problems [17–19]. The success of these software tools lies in their usability. A user-friendly environment and the implementation in commonly used software are key factors for their success and popular use. For example, groundwater studies usually employ Geographic Information Systems (GIS) since they are powerful, widely available tools that can deal with large amounts of spatial georeferenced information [20] and make calculations in an efficient way to provide quick results [21]. The analysis and mapping of hydrogeological data provide useful spatiotemporal information to decision makers [22].

GIS tools have been widely used for different purposes related to groundwater issues [23, 24]. Several authors have developed different source codes within GIS environment (Alcaraz et al. [25] Bhatt et al. [26]) for hydrogeological modelling. A free and open source module included in FREEWAT was developed by Criollo et al. [19] to analyze hydrochemical and hydrogeological data in order to simplify the characterization of the groundwater bodies at chemical risk. Almeida et al. [27] coupled a groundwater flow model into a GIS environment to simulate transient flow in a confined aquifer. Akbar et al. [28] and Ríos et al. [29] presented GIS-based models to simulate contaminants leaching into groundwater.

In coastal areas, a three-dimensional GIS-based groundwater flow model was developed [30] to simulate the aquifer’s response to past climate changes. A new ArcGIS tool for groundwater flow simulation and visualization of results was also implemented by [19]. Other authors (De Filippis et al. [31]) applied a previously developed GIS-based tool (AkvaGIS), in addition to a groundwater flow model, to study the impacts of pumping on seawater intrusion in coastal aquifers in Malta and Italy. This tool was used in other studies (Perdikaki et al. [32]) to analyze hydrochemical parameters in a coastal aquifer that presented seawater intrusion problems.

Nevertheless, as far as we know, there is no ArcGIS tool focusing on analyzing seawater intrusion (SWI) status and vulnerability at aquifer scale.

In this paper, we describe the development of a new ArcGIS tool called GIS-SWIAS, which is the implementation of the index-based method for assessing aquifer status and vulnerability to SWI proposed by [13, 15]. It helps to analyze SWI status and/or vulnerability at aquifer scale using a mixed lumped-distributed analysis. It is a user-friendly ArcGIS® toolbox that performs all the required calculations for specific dates or temporal periods inside a GIS environment. The data inputs to the model are hydraulic head and chloride concentration maps. The tool provides two options to map these variables: the first is to use point data by applying interpolation techniques integrated within GIS-SWIAS, while the second is to take these data from existing external distributed models. The second approach allows different climatic and/or management scenarios to be assessed and compared. From those maps, extensive calculations have been fully automatized in GIS-SWIAS to display the results as distributed maps of affected and nonaffected volumes (at a specific moment or over a period of time), mean conceptual cross sections, and a lumped index (Ma and L_Vul) to analyze the global intensity and the dynamics of SWI.

Although there are many GIS-based tools in the literature that allow simulating groundwater flow and analyzing groundwater quality, none of them perform spatial and temporal analysis on groundwater quality and vulnerability issues. Moreover, this new tool provides simple pictures that summarize the proportional affected area within the aquifer according to a chloride threshold. For this purpose, GIS-SWIAS has been applied to analyze the seawater intrusion problem, but this tool could be applied to represent the global status of an aquifer to any contaminant. The main objective of GIS-SWIAS is to provide an easy-to-use tool through a user-friendly interface that can be used by users at different levels of expertise to summarize the SWI problem at aquifer scale. It allows analyzing long time periods with a low computing cost.

3. Description of GIS-SWIAS Tool: Models, Inputs, and Outputs

GIS-SWIAS is an ArcGIS ArcToolbox that contains the models required to analyze SWI status and vulnerability at aquifer scale, according to the methodology described in previous works [13, 15]. Figure 1 shows the structure of the tool, which includes inputs, steps, and models, as well as the outputs generated.

To determine the overall status of the aquifer, the inputs to the tool include variables (to characterize the historical evolution of hydraulic head and chloride concentration) and parameters (to define aquifer geometry and hydrodynamic behaviour). Data describing the historical evolution can come from direct observations (monitoring network) or other techniques (geophysical applications, etc.). For the vulnerability assessment, an additional input is required: a distributed vulnerability index map, which comes from other intrinsic information (aquifer type, conductivity, distance from the shoreline, and bicarbonate concentration).

The results/outputs produced to summarize status and vulnerability to SWI through visual displays and time series are as follows: (1) maps of aquifer volumes affected by SWI (2) 2D conceptual cross sections (with mean penetration inland and mean thickness on specific dates, or mean values over a period of time) (3) lumped index (mass of chloride that causes the concentration in some areas to exceed the SWI threshold and lumped vulnerability index) to summarize the global dynamic of SWI within the aquifer.

3.1. Description of the Outputs: Theoretical Background

In order to assess the maps of SWI-affected aquifer volumes for different dates, we need to compile (A) maps of chloride concentration or vulnerability to SWI (B) maps of groundwater volumes for specific dates (C) threshold of chloride concentration or vulnerability that will be used to tag which parts of the aquifer are impacted (areas with chloride concentration or vulnerability index exceeding the threshold). The tool provides two options with respect to the input maps (A) (chloride or vulnerability maps) and (B), either calculating the maps internally from point data by applying interpolation techniques integrated within GIS-SWIAS or taking the maps from existing external distributed models the second option means that various potential climatic and/or management scenarios can be compared and assessed. Maps of groundwater volume are calculated by combining hydraulic head, geometry, and the storage coefficient. The maps of groundwater volume and chloride concentration are combined to assess the aquifer volume affected by using a chloride threshold (Vr). This threshold is assumed to be equal to the natural background level of the aquifer, or the reference quality standard determined by authorities in order to maintain a good groundwater status. The affected volume is defined as the groundwater volume of resource whose chloride concentration is above the established threshold.

2D conceptual cross section depicts the magnitude of the intrusion process in the aquifer at a specific moment, or the mean values in a period of time. The cross section is defined orthogonal to the shoreline. It summarizes the mean geometry of the affected volume, i.e., the mean thickness and inland penetration of the aquifer volume with chloride concentration above the threshold. The average affected thickness Tha(m) and inland penetration P(m) of the intrusion can be calculated by summing the values in each cell i of the aquifer mesh where the chloride concentration exceeds the threshold:

where Vi(>Vr) is the groundwater volume (m 3 ) in the cell i with a chloride concentration (or vulnerability) exceeding Vr Si is the surface area (m 2 ) of the cell i with chloride concentration (or vulnerability) exceeding Vr bi is the saturated thickness (m) within the cell i with Cl concentration (or vulnerability) above Vr αi is the storage coefficient in the cell i Lcoast is the length of coastline (m).

The mean chloride concentration (C) of the affected volume is

where is the chloride concentration (mg/l) in cell i Vi(>Vr) is the groundwater volumes (m 3 ) in cell i with a concentration exceeding Vr is the total groundwater volume (m 3 ) of the affected area.

The increment of chloride concentration (IC) above the threshold (Vr) in the affected volume is

Both variables, the conceptual cross section and IC index, give an overview of the magnitude and intensity of the intrusion process per linear metre of coast at a specific moment in time.

The lumped index Ma (mass of chloride that causes the concentration in some areas to exceed the threshold) to summarize the global dynamic of SWI within the aquifer is obtained multiplying the increment of concentration (IC) by penetration (P) and affected thickness (Tha) from (1) and (3).

The vulnerability to SWI (or vulnerability to contamination in general) is assessed and summarized following the same steps to assess the SWI status. In this case, instead of chloride concentration values, a distributed map of groundwater vulnerability has to be generated by applying any index-based method (e.g., GALDIT) and the threshold applied to identify the affected area is defined by a specific vulnerability class (e.g., high or very high vulnerability).

The affected volume corresponds to the groundwater that presents vulnerability values above the threshold (e.g., very high vulnerability). The average affected thickness Tha(m) and inland penetration P(m) are calculated by applying (1).

The lumped index to assess vulnerability is

where is the value of the vulnerability index (-) in cell i.

The concept of Ma and L_Vul involves some simplifications, in accordance with the definition of the conceptual cross sections. While 2D maps and cross sections summarize the extent and magnitude of SWI and vulnerability in an aquifer at a specific time, Ma and L_Vul indices show the lumped intensity and temporal dynamic of the SWI and vulnerability to SWI at aquifer scale.

3.2. Tool Programming in ArcGIS

GIS-SWIAS is an ArcGIS ArcToolbox composed of a chain of models programmed in ModelBuilder. ModelBuilder is a visual programming language that allows chaining and sequencing several geoprocessing ArcGIS tools through a user-friendly interface. ModelBuilder is available within the tool bar in ArcGIS. It allows the addition of any geoprocessing tool of ArcGIS by linking and providing its output and transferring it to another tool as input.

Programming in ModelBuilder allows us to automate a workflow to create a model, which can be documented and shared as a ArcGIS tool. ModelBuilder contains a script tool to link with Python scripts and other models. It also allows iteration of a workflow, so it can be very useful to analyze the evolution of the historical hydrogeological processes.

The three models that compose GIS-SWIAS have been compiled by adding different tools from ArcToolbox to create shapes from point data, to interpolate, etc. Figure 2 shows the workflow of one of the three models.

Although ModelBuilder is an intuitive and easy-to-use tool, the integration of lots of geoprocesses in the same model may be difficult. Because several geoprocesses have dynamic parameters and the user interaction is necessary, GIS-SWIAS was divided into three steps (models) that have to be executed following the workflow shown in Figure 1.

3.3. Description of the Models within GIS-SWIAS

GIS-SWIAS contains three ModelBuilder models (Figure 2) that can be applied individually or used sequentially to produce a complete lumped assessment of the SWI at aquifer scale. GIS-SWIAS can be shared with other users and it can be added as a toolbox as shown in Figure 3. The run sequence follows the order shown in the workflow (Figure 1): “Chloride concentration map”, “Hydraulic head map,” and “Summarizing SWI”. These models can be run within the ArcToolbox window providing a user-friendly graphical user interface.

3.3.1. “Chloride Concentration map” Model

The “Chloride concentration map” model generates a classified chloride concentration shapefile from a point feature table in text format by using Inverse Distance Weighting (IDW) interpolation technique (other interpolation techniques could be implemented in this tool). It can also import chloride concentration fields from Visual MODFLOW files. The dialog box shown in Figure 4 is opened by double-clicking the model tool on the ArcToolbox window.

The model requires a polygon shapefile of the aquifer and the workspace containing a text file for each date to be analyzed. The text files have to include X and Y UTM coordinates that define the locations of the point features and chloride concentration values (mg/l) in the observation wells. Text files also have to include column header, as shown in Figure 4. The text filename cannot exceed eight characters nor include blank spaces or special characters (underscore can be used as a substitute). It is not necessary that all points contain data every date of the period to be analyzed.

The user has to indicate the fields (columns) in the input table that contain the X and Y coordinates and the chloride concentration value for each point (Figure 5). Optional settings concerning IDW interpolation techniques can be changed by the user. A reclassification of the values after interpolation is also required. Finally, the user has to choose a folder where the output shapefiles will be saved.

When all the required parameters are filled out, the model can be executed by clicking “OK” at the bottom of the dialog box. The execution screen (Figure 6) shows the running processes and it can be closed when the execution has successfully completed.

The “Chloride concentration map” model provides the following shapefiles for each date analyzed: (1) point shapefile of chloride concentration data (2) raster from data interpolation (3) polygon shapefile from interpolation covering the default extension and (4) polygon shapefile from interpolation clipped to the shape of the aquifer.

3.3.2. “Hydraulic Head Map” Model

The “Hydraulic head map” model generates a classified hydraulic head shapefile from a point feature table in text format. It also generates a shapefile containing aquifer variables (chloride concentration and hydraulic head values) and aquifer parameters (storage coefficient and bottom of the aquifer). The dialog box is shown in Figure 7.

This model has the same input requirements as for the “Chloride concentration map” model but focuses on hydraulic head data (m.a.s.l.). It also allows hydraulic head fields to be imported from Visual MODFLOW. The name of hydraulic head text files must be the same as the chloride concentration text files for each time period analyzed.

The user has to indicate the location of the chloride shapefiles generated in the previous model (“Chloride concentration map” model). It also requires polygon shapefiles of storage coefficient and bottom (m) of the aquifer as inputs.

The model generates shapefiles of hydraulic head data in an analogous way to the “Chloride concentration map” model. Moreover, it provides a polygon shapefile containing variables for each date analyzed (chloride concentration and hydraulic head values) and parameters (bottom and storage coefficient) of the aquifer. This shapefile is named “union_%name of the hydraulic head text file%_hh.shp”, where “% name of the hydraulic head text file%” is variable if different dates are analyzed.

3.3.3. “Summarizing SWI” Model

For the “Summarizing SWI” model, the methodology proposed in Baena-Ruiz et al. [13,15] and described in Section 3.1 has been implemented in the ArcGis environment. This tool generates Excel® tables containing statistics that summarize SWI at aquifer scale. It also generates conceptual cross sections (.shp), where the mean affected and nonaffected volumes are drawn for the aquifer (average values over a time period or instantaneous values on a specific date). If different dates are analyzed, it shows graphs representing the temporal evolution of Pa and Ta variables, percentage of affected volume, chloride concentration within the aquifer, and Ma index (or lumped vulnerability index). The dialog box for global status assessment is shown in Figure 8.

The “Summarizing SWI” model requires the folder path where the results of the “Hydraulic head model” have been previously saved to be specified. In this folder, the shapefile named “union_%name of the hydraulic head text file%_hh.shp” contains chloride concentration, hydraulic head, bottom of the aquifer, and storage coefficient fields. The user has to select from the pull-down list the corresponding column in the input shapefile for each field, as shown in Figure 9.

The next required parameter in this tool is the “Chloride threshold.” It is defined as the chloride concentration value above which the aquifer is considered to be affected by SWI. This threshold may be set as the natural background level of the aquifer or as the relevant environmental quality standards. Hinsby et al. [14] proposed a method to calculate groundwater thresholds values based on these criteria.

The shoreline length (m) is also required for subsequent calculations.

X and Y axes establish the coordinate system of the conceptual cross sections. The GIS-SWIAS tool provides polyline shapefiles for X and Y axes located at (0,0), but the user can translate them to another coordinate origin or create new ones.

The vertical scale factor is used to rescale the vertical magnitude (Ta) of the conceptual cross section if the factor Ta/Pa is too small. If vertical scale factor = 1, the conceptual cross section will maintain the real size ratio.

Finally, two paths where the output results will be saved are required. “Output workspace statistic” will contain lumped variables reports in Excel table format for each date analyzed (Figure 10) and mean statistics for the entire period. Four graphs will be also saved in this path: (1) temporal evolution of Pa and Ta variables (2) percentage volume affected (3) Ma index and (4) chloride concentration within the aquifer (mean chloride concentration in the aquifer, mean chloride concentration in the affected volume, and the increment of concentration within the affected volume above the threshold).

“Output workspace results” will contain the polygon shapefiles that allow the (1) mean affected and (2) nonaffected conceptual cross section within the aquifer for each date analyzed to be drawn, (3) the mean affected and (4) nonaffected conceptual cross section within a time period, and the (5) maximum affected cross section for a time period. These two paths can be the same for all results, but they have to be different from the output paths of the previous models.

Figure 11 and Table 1 show the graphical and statistical summaries, respectively, from the GIS-SWIAS tool.

Lumped variables (Excel table)
At a specific moment in timeStatistics for a time period
Total aquifer volumeAverage aquifer volume
Total aquifer affected volumeAverage aquifer affected volume
Total chloride concentration

The GIS-SWIAS tool can provide results for each date where information is available these are obtained by iterative application of the described method. GIS-SWIAS allows historical [13] and future periods [15] to be analyzed if the hydraulic head and chloride maps come from a density-dependent flow model. By this means, GIS-SWIAS can be used to analyze adaptation strategies [15] in terms of reducing SWI, taking into account future potential scenarios that might include CC and/or GC, also considering projected land use change scenarios (new urbanized areas, crop transformations) [15].

Moreover, this tool may be also used to summarize SWI vulnerability, for any index method applied to assess it. In this case, instead of the chloride concentration maps, generated by executing the “Chloride concentration map” model, polygon shapefiles of the vulnerability index (previously prepared by the user) would be used as inputs of the model “Summarizing SWI vulnerability” (Figure 12), which also will require the “Hydraulic head map” shapefile generated by the tool, as previously described.

The vulnerability index maps must be included as numerical fields (values obtained before defining the vulnerability classes). In order to generate the conceptual cross sections that summarize the “affected” aquifer volume, i.e., where the vulnerability to SWI is identified, the tool requires a vulnerability threshold to be input that represents the reference value chosen to distinguish between affected and nonaffected volumes. This threshold will be also used to assess the lumped vulnerability index.

Just as in the definition of the Ma index, the lumped global value of vulnerability in the aquifer on a specific date is obtained by weighting the vulnerability score in each cell with its water storage. This lumped index also allows an analysis of the dynamic of SWI vulnerability of the system at aquifer scale to be performed. The lumped index can be also obtained using different threshold values [13, 15].

4. Discussion

GIS-SWIAS is a user-friendly polyvalent ArcGIS tool that provides a comprehensive overview of SWI status and vulnerability at aquifer scale. It integrates three models, which are documented in order to briefly explain the tool’s description, its utility, and the data required for each item. This tool can be applied by scientists and decision makers, who may not be advanced users of GIS, to summarize SWI problems. Many GIS-based tools have demonstrated to be powerful and cost-effective to analyze groundwater issues (Criollo et al. and Perdikaki et al. [19, 32]). Moreover, GIS models as ModelBuilder models can be integrated into other platforms by using the Python script tool (Menezes and Inyang [33]).

Due to the heterogeneous distribution of seawater intrusion, distributed information and assessments are required to study its impacts [8, 30]. For this reason, the methods for modelling [34, 35] SWI impacts and the user-friendly tools developed based on them [36–38] also require distributed inputs and calculations. The GIS-SWIAS is a tool that could be classified as a postprocessing tool to summarize and help in the analysis of SWI impacts at aquifer scale. This tool produces both distributed and lumped results at aquifer scale, but, logically, it also requires distributed inputs and assessments, as described in the previous sections. In this group of postprocessing tools, we find in the literature, for example, [39]. GIS-SWIAS is a new tool, in which the method proposed by [13, 15] has been implemented. A significant novelty of this method with respect to other previously developed methods is that the proposed lumped index to summarize SWI status at aquifer scale is based on a variable with physical meaning (mass of chloride that causes the concentration in some areas to exceed the natural threshold). On the other hand, a novel aspect of this tool is that, from the distributed information and calculations, GIS-SWIAS allows easy computation of the affected volume containing a chloride concentration above a threshold. This tool also helps to produce lumped SWI outputs (indices) at aquifer scale. It produces valuable information that helps draw conclusions about the dynamic at aquifer scale, in terms of affected volume and global SWI intensity. Thus, it also provides insight into aquifer resilience and trends. Therefore, it will help to identify coastal groundwater bodies that require new management strategies to be implemented to achieve a good status.

The identification of SWI (the phenomenon that we want to analyze) requires a threshold value established that defines the inflection point beyond which the aquifer begins to register an impact. Previous research shows that the impact of SWI is significantly sensitive to the choice of the threshold value adopted [13]. The significant uncertainties around determining these threshold values [14] and the sensitivity of whether the aquifer is reported as being impacted by SWI or not increase the practical interest of the GIS-SWIAS tool: it is capable of performing the extensive calculations required to summarize SWI at aquifer scale, for the analyses of both historical and potential scenarios, considering different threshold values, which allow the comparison of the results.

With respect to the maps employed as inputs, the tool allows two options: to generate maps from available data using different interpolation techniques integrated in the tool and to take the maps directly from SEAWAT files. This functionality—which allows maps to be generated from point data or to be loaded from other commonly employed tools—has also been implemented in other SWI assessment tools [36, 37]. However, as far as we know, it is not available in postprocessing tools. In cases where map inputs are taken from density-dependent models, a comparative assessment of different scenarios (climatic conditions and/ or management strategies) could be performed. The physical-process approach can be applied to propagate and compare various potential conditions, and so in this case maps can be obtained and compared for different scenarios (e.g., management scenarios or future potential CC scenarios) this means that the output of the tool can support the decision-making process [15]. In contrast, when the maps employed to define the indices are obtained by applying simple interpolation approaches, analysis is limited to the historical period for which the data are available.

The tool also helps to analyze the vulnerability to seawater intrusion at aquifer scale. In the literature, we find different methods to assess groundwater vulnerability depending on the drivers of pollution (Aller et al., Vias et al., and Baena-Ruiz and Pulido-Velazquez [40–42]), pumping (Pulido-Velazquez et al. [43, 44]), and SWI [12, 16]. User-friendly tools have appeared to assist in this assessment some of them were developed in a GIS environment [45]. Nevertheless, there are no tools that help in the assessment of SWI vulnerability with that focus on postprocessing. Therefore, this is the first postprocessing tool described that integrates SWI status and vulnerability assessment, which is very valuable information to help identify the significance of SWI problems in aquifers and potential sustainable solutions.

The GIS-SWIAS tool has been applied to two different case studies in the Mediterranean area of Spain (Plana de Oropesa Torreblanca and Plana de Vinaroz), obtaining the results described in previous papers [13, 15]. In [13], the process automation to generate the interpolated maps enabled authors to analyze SWI status and vulnerability over an extended time period (1977–2015) and to prove the sensitivity of results to the chloride threshold value (two threshold values were analyzed: 250 mg/l and 1100 mg/l) in Plana de Oropesa-Torreblanca and Plana de Vinaroz aquifers. In [15], the impacts of future GC scenarios were analyzed in Plana de Oropesa-Torreblanca aquifer. The methodology from [13] was adapted to compare six potential future scenarios including adaptation strategies. The historical period came from 1973 to 2010 and the six future scenarios covered the period 2011–2035.

The underlying methodology implemented in GIS-SWIAS was applied in [13] by interpolating chloride maps and hydraulic head from observation points, whereas the information to generate the field maps in [15] was loaded from SEAWAT model. The results of these studies for the Plana de Oropesa-Torreblanca aquifer show differences that reveal that the physical-process SWI approximations obtained using the density-dependent flow model give a more accurate representation. Despite these differences, the results are in the same order of magnitude. Other authors who have developed indices related to SWI [6, 9] have also proved that results do not differ considerably by including three-dimensional salinity data. Furthermore, the approximation obtained using interpolation will depend on the number of observations and the distribution of these points within the aquifer.

Although this tool has been developed to analyze SWI problem, it could be applied to study the lumped impact of any contaminant on groundwater and/or the groundwater vulnerability by applying any vulnerability index. In this case, instead of the “Shoreline length” parameter to generate the cross section, other equivalent lengths (e.g., the aquifer length orthogonal to the groundwater flow direction) should be considered. Therefore, GIS-SWIAS fulfils the requirements of flexibility, sturdiness, easy interaction, and user-friendliness, which make it a useful tool in the decision-making process. It will allow using them as “share vision models/tools” to help in the discussion of management alternatives between stakeholders and administration representatives [46]. Many Decision Support Systems tools were not successful because they were not user-friendly [47, 48].

4.1. Assumptions and Limitations

In this section we summarize the main assumptions/limitations of the GIS-SWIAS tool and in the implemented methodology.

4.1.1. Underlying Methodology
4.1.2. GIS-SWIAS Tool

5. Conclusions

In this paper we describe a new general tool, GIS-SWIAS. It is an ArcGIS-based tool, designed to analyze SWI status and vulnerability at aquifer scale by applying the method presented by [13, 15]. It is a user-friendly tool that allows georeferenced information to be dealt with, and it is easy to introduce the required data (inputs) and to efficiently perform the demanding computational operations required. Its outputs are in the form of reports and images to summarize the magnitude, intensity, and temporal evolution of SWI within an aquifer.

The GIS-SWIAS tool can be applied to assess historical SWI dynamic in case studies where we do not have a previous model. Nevertheless, if we want to analyze in a rational quantitative analysis of the various alternative management scenarios to manage SWI in a sustainable manner, the GIS-SWIAS tool will need to take information on hydraulic head and chloride concentration distribution generated from simulations of their impacts by a calibrated density-dependent flow model. In such cases, adaptation strategies to potential future scenarios, whose distributed impacts have to be propagated within the previously calibrated models, could usefully be analyzed and compared using this tool. GIS-SWIAS can be applied to assess not only SWI status at aquifer scale, but also vulnerability to any contaminant.

Given all these ways that the GIS-SWIAS tool can be applied, it provides a valuable tool for both researcher and technician to assess SWI dynamics and aquifer resilience under different management scenarios. It can support the decision-making process in the rational selection of sustainable management strategies. The tool’s performance has been tested and confirmed in two case studies described in previous research works.

It can be applied to any case study. The easy-to-use workflow and the few input data required facilitate its application to a large number of case studies in order to compare SWI.

Data Availability

The software developed in this study may be released upon application to the authors, who can be contacted at [email protected] or [email protected]

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This work has been supported by the GeoE.171.008-TACTIC and GeoE.171.008-HOVER projects from GeoERA Organization funded by European Union’s Horizon 2020 Research and Innovation Program and SIGLO-AN (RTI2018-101397-B-I00) project from the Spanish Ministry of Science, Innovation and Universities (Programa Estatal de I+D+I orientada a los Retos de la Sociedad).

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Copyright

Copyright © 2021 Leticia Baena-Ruiz and David Pulido-Velazquez. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


ModelBuilder Tool produces different results when using Run Button and running as a tool - Geographic Information Systems

You can use Simulink ® to model a system and then simulate the dynamic behavior of that system. The basic techniques you use to create a simple model in this tutorial are the same as those you use for more complex models. This example simulates simplified motion of a car. A car is typically in motion while the gas pedal is pressed. After the pedal is released, the car idles and comes to a stop.

A Simulink block is a model element that defines a mathematical relationship between its input and output. To create this simple model, you need four Simulink blocks.

Block NameBlock PurposeModel Purpose
Pulse Generator Generate an input signal for the modelRepresent the accelerator pedal
Gain Multiply the input signal by a constant valueCalculate how pressing the accelerator affects the car acceleration
Integrator, Second-Order Integrate the input signal twiceObtain position from acceleration
Outport Designate a signal as an output from the modelDesignate the position as an output from the model

Simulating this model integrates a brief pulse twice to get a ramp. The results display in a Scope window. The input pulse represents a press of the gas pedal — 1 when the pedal is pressed and 0 when it is not. The output ramp is the increasing distance from the starting point.

Open New Model

Use the Simulink Editor to build your models.

Start MATLAB ® . From the MATLAB toolstrip, click the Simulink button .

Click the Blank Model template.

The Simulink Editor opens.

From the Simulation tab, select Save > Save as. In the File name text box, enter a name for your model. For example, simple_model . Click Save. The model is saved with the file extension .slx .

Open Simulink Library Browser

Simulink provides a set of block libraries, organized by functionality in the Library Browser. The following libraries are common to most workflows:

Continuous — Blocks for systems with continuous states

Discrete — Blocks for systems with discrete states

Math Operations — Blocks that implement algebraic and logical equations

Sinks — Blocks that store and show the signals that connect to them

Sources — Blocks that generate the signal values that drive the model

From the Simulation tab, click the Library Browser button .

Set the Library Browser to stay on top of the other desktop windows. On the Simulink Library Browser toolbar, select the Stay on top button .

To browse through the block libraries, select a category and then a functional area in the left pane. To search all of the available block libraries, enter a search term.

For example, find the Pulse Generator block. In the search box on the browser toolbar, enter pulse , and then press Enter. Simulink searches the libraries for blocks with pulse in their name or description and then displays the blocks.

Get detailed information about a block. Right-click the Pulse Generator block, and then select Help for the Pulse Generator block. The Help browser opens with the reference page for the block.

Blocks typically have several parameters. You can access all block parameters by double-clicking the block.

Add Blocks to a Model

To start building the model, browse the library and add the blocks.

From the Sources library, drag the Pulse Generator block to the Simulink Editor. A copy of the Pulse Generator block appears in your model with a text box for the value of the Amplitude parameter. Enter 1 .

Parameter values are held throughout the simulation.

Add the following blocks to your model using the same approach.

Add a second Outport block by copying the existing one and pasting it at another point using keyboard shortcuts.

Your model now has the blocks you need.

Arrange the blocks by clicking and dragging each block. To resize a block, drag a corner.

Connect Blocks

Connect the blocks by creating lines between output ports and input ports.

Click the output port on the right side of the Pulse Generator block.

The output port and all input ports suitable for a connection are indicated by a blue chevron symbol .

Point to to see the connection cue.

Click the cue. Simulink connects the blocks with a line and an arrow indicating the direction of signal flow.

Connect the output port of the Gain block to the input port on the Integrator, Second-Order block.

Connect the two outputs of the Integrator, Second-Order block to the two Outport blocks.

Save your model. In the Simulation tab, click Save.

Add Signal Viewer

To view simulation results, connect the first output to a Signal Viewer .

Click the signal. In the Simulation tab under Prepare, click Add Viewer. Select Scope. A viewer icon appears on the signal and a scope window opens.

You can open the scope at any time by double-clicking the icon.

Run Simulation

After you define the configuration parameters, you are ready to simulate your model.

In the Simulation tab, set the simulation stop time by changing the value in the toolbar.

The default stop time of 10.0 is appropriate for this model. This time value has no unit. The time unit in Simulink depends on how the equations are constructed. This example simulates the simplified motion of a car for 10 seconds — other models could have time units in milliseconds or years.

To run the simulation, click the Run button .

The simulation runs and produces the output in the viewer.

Refine Model

This example takes an existing model, moving_car.slx , and models a proximity sensor based on this motion model. In this scenario, a digital sensor measures the distance between the car and an obstacle 10 m (30 ft) away. The model outputs the sensor measurement and the position of the car, taking these conditions into consideration:

The car comes to a hard stop when it reaches the obstacle.

In the physical world, a sensor measures the distance imprecisely, causing random numerical errors.

A digital sensor operates at fixed time intervals.

Change Block Parameters

To start, open the moving_car model. At the MATLAB command line, enter:

You first need to model the hard stop when the car position reaches 10 . The Integrator, Second-Order block has a parameter for that purpose.

Double-click the Integrator, Second-Order block. The Block Parameters dialog box appears.

Select Limit x and enter 10 for Upper limit x. The background color for the parameter changes to indicate a modification that is not applied to the model. Click OK to apply the changes and close the dialog box.

Add New Blocks and Connections

Add a sensor that measures the distance from the obstacle.

Modify the model. Expand the model window to accommodate the new blocks as necessary.

Find the actual distance. To find the distance between the obstacle position and the vehicle position, add the Subtract block from the Math Operations library. Also add the Constant block from the Sources library to set the constant value of 10 for the position of the obstacle.

Model the imperfect measurement that would be typical to a real sensor. Generate noise by using the Band-Limited White Noise block from the Sources library. Set the Noise power parameter to 0.001 . Add the noise to the measurement by using an Add block from the Math Operations library.

Model a digital sensor that fires every 0.1 seconds. In Simulink, sampling of a signal at a given interval requires a sample and hold. Add the Zero-Order Hold block from the Discrete library. After you add the block to the model, change the Sample Time parameter to 0.1 .

Add another Outport to connect to the sensor output. Keep the default value of the Port number parameter.

Connect the new blocks. The output of the Integrator, Second-Order block is already connected to another port. To create a branch in that signal, left-click the signal to highlight potential ports for connection, and click the appropriate port.

Annotate Signals

Add signal names to the model.

Double-click the signal and type the signal name.

To finish, click away from the text box.

Repeat these steps to add the names as shown.

Compare Multiple Signals

Compare the actual distance signal with the measured distance signal.

Create and connect a Scope Viewer to the actual distance signal. Right-click the signal and select Create & Connect Viewer > Simulink > Scope. The name of the signal appears in the viewer title.

Add the measured distance signal to the same viewer. Right-click the signal and select Connect to Viewer > Scope1. Make sure that you are connecting to the viewer you created in the previous step.

Run the model. The Viewer shows the two signals, actual distance in yellow and measured distance in blue.

Zoom into the graph to observe the effect of noise and sampling. Click the Zoom button . Left-click and drag a window around the region you want to see more closely.

You can repeatedly zoom in to observe the details.

From the plot, note that the measurement can deviate from the actual value by as much as 0.3 m. This information becomes useful when designing a safety feature, for example, a collision warning.


4 Answers 4

  1. Install R-4.0.0
  2. Install Rtools35
  3. Edit $R_HOME/etc/x64/Makeconf (for R-4.0.0-x64)
  4. Rcmd INSTALL RDCOMClient

Rik's answer was incredibly helpful and got a version working however, after spending a day on it, I was able to improve on it. I want to put that here in case I have to do it again. The main improvement is being able to build a working package for both 32- and 64-bit architectures. By default, R installs both, and this makes things easier when installing dependent packages.

The first two steps are the same:

If (like me) you had already installed rtools40, a system environment variable named RTOOLS40_HOME is created. The first step is to change that to:

If you don't have rtools40 installed, then create the RTOOLS40_HOME system environment variable.

Two changes are still needed in the make files. These are found in your R installation directory.

In etcx64Makeconf , add underscores to match the rtools35 directory structure by setting these values:

Do the same in etci386Makeconf :

Do not set BINPREF as an environment variable, or this will overwrite the makefile changes (like RTOOLS40_HOME does). With these complete, finish off with the same steps that Rik outlined:

Open windows command prompt and change to the directory that contains the RDCOMClient subdirectory and type:

R CMD INSTALL RDCOMClient –-build RDCOMClient.zip

This installs RDCOMClient in the local installation of R-4.0.0 and additionally creates the file RDCOMClient_0.94-0.zip that can be installed on other systems using the following command:


ModelBuilder Tool produces different results when using Run Button and running as a tool - Geographic Information Systems

Structure of the MPM Online Tool

- The MPM online tool is a geographic information systems (GIS) modelling tool for modelling of mineral prospectivity in northern Finland.

- The MPM online tool is composed of GTK open source geospatial datasets and fuzzy logic modelling tools.

- The input data i.e. raster layers and other GIS dataset helping to localize and assess modelling results are located on the left hand side of the tool under the 'Layers' (Fig. 1). 'Derivatives' are generated from the geological GIS data such as distance to structures and density of structures. The airborne geophysical data consists of magnetic and electromagnetic interpolated measurement data. The geochemical data is interpolated from glacial till assay data (see Data for specifications). Unfortunately, in the current version of the MPM online tool the histogram stretching of the colour scale cannot be performed by the user.

- The spatial representation of the input and output layers, and other exploration related data layers, can be viewed in the 'Map' (Fig. 1).

- Fuzzy tools i.e. 'Fuzzy Membership' and 'Fuzzy Overlay' tools are located on the top right corner of the MPM online tool (Fig. 1).

Fig. 1. The user interphase of the Mineral Prospectivity Modeler Online Tool.

- To produce a prospectivity model, the input data and fuzzy modelling tools are arranged into a geoprocessing model to the 'Model Builder' located on the right hand side of the MPM online tool (Fig. 2).

- 'Bookmarks' at the bottom left of the MPM online tool can help to zoom into a focus area.

Construction of a mineral prospect model in the Model Builder

- The input data and fuzzy tools can be dragged with the left hand mouse button and dropped one dataset or a tool at a time to the Model Builder (Fig. 2). Copy-pasting of the data ellipses and tool rectangles is not possible in the current version of the MPM tool.

- A raster input and output can be removed by clicking on to the raster ellipse (outlines turn green) and pressing the delete button on the keyboard.

- An input raster dataset can be connected to a Fuzzy Membership tool by bringing the cursor on top of an ellipse representing the input raster (see Fig. 3). When the cursor turns into a hand icon click onto ellipse with the right mouse button. Keep the right mouse button down and move the cursor onto the Fuzzy Membership tool. Release the mouse button and the input raster layer and the fuzzy tool should be connected with an arrow.

Figure 3. Connect an input layer ( ap_resistivity ) to the Fuzzy Membership tool by drawing an arrow in the Model Builder. The output of the Fuzzy Membership in this example case is raster( 15).

- For the modelling result to be meaningful each input dataset has to be transformed with Fuzzy Membership tool before Fuzzy Overlay. The fuzzy logic as an expert driven machine learning technique is described in 'Fuzzy logic' and the details of the fuzzy tools at http://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/an-overview-of-the-overlay-tools.htm

- The membership is always scaled between 0 and 1. Thus, if the real minimum membership is more than 0 or the real maximum less than 1, the memberships have to be transformed to the correct range using other tools which are not yet available in the MPM Online Tool.

- The Fuzzy Membership tool parameters are shortly described in table 1. For more detailed information go to http://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/fuzzy-membership.htm and click to see the specifications for each function.

Fuzzy membership tool parameter

The fuzzy membership transformation function 'Large' defines the shape of the fuzzy membership function as an S-shape increasing function. The small values of input data will be close to 0 and large values close to 1.

Defines the shape of the fuzzy membership function as an Gaussian bell shaped function. The small and large values of input data will be close to 0 and values close the mid point close to 1.

Near function is similar to the Gaussian fuzzy membership function except the Near function has a more narrow spread.

Defines the shape of the fuzzy membership function as an S-shape decreasing function. The small values of input data will be close to 1 and large values close to 0.

Defines the value of the input data with a fuzzy membership of 0.5. The default value of the mid point is mean of the dataset if the mid point field in the tool is left empty. The processing extent is considered when the mean value is calculated.

Defines the spread of a function. For the Large and Small functions the spread values range from 1 to 10, for Gaussian from 0.01 to 1 and for Near from 0.001 to 1. Larger values result in a steeper distribution from the midpoint.

Defining a hedge increases or decreases the fuzzy membership values which modify the meaning of a fuzzy set. Hedges are useful to help in controlling the criteria or important attributes.

NONE -No hedge is applied. This is the default.

SOMEWHAT 'Known as dilation, defined as the square root of the fuzzy membership function. This hedge increases the fuzzy membership functions.

VERY -Also known as concentration, defined as the fuzzy membership function squared. This hedge decreases the fuzzy membership functions.

Table 1. Parameters of the Fuzzy Membership functions and their specifications.

- To specify the parameters a Fuzzy Membership tool can be opened by double clicking the Fuzzy Membership rectangle. An additional window opens up to specify the parameters (Fig. 4).

Figure 4. The Fuzzy Membership tool. Specify the Fuzzy Membership type, Mid point , Spread and Hedge.

- Determination of the midpoint is critical for the success of the modelling. In the current version of the MPM online tool, there is no tool to study the distribution of the data e.g. as a histogram or descriptive statistics besides the mean. If the midpoint field in the Fuzzy Membership tool is left empty the tool uses mean of the input data as a mid point . In this case, the chosen extent is considered. It is an advisable practice to run a Fuzzy Membership tool first with an empty mid point field and keep notes of the used mid point which is reported in the 'Running model. '-window and updated back to tool if it was originally left empty. This way the user will know the mean value of the data and can then start to increase or decrease it manually for the following runs of the model.

- Connect the Fuzzy Membership output rasters to each other with Fuzzy Overlay tool. The assumption is that the user has scaled the inputs between 0 and 1 prior to combining them with a Fuzzy Overlay function.To specify the parameters the Fuzzy Membership tool can be opened by double clicking the Fuzzy Overlay rectangle. An additional window opens up to specify the parameters (Fig. 5).

- The Fuzzy Overlay types and their short definition is given in table 2.

- To view active parameters of the tool, hover the mouse over tool and the tooltip opens.

Returns the minimum value of all of the input evidence rasters for each cell.

Returns the maximum value of all of the input evidence rasters for each cell.

Calculates the product of unfavourabilities of all the input rasters and subtracts this from unity for each cell. Tends towards large values if even one of the inputs has a large value or if many inputs have intermediate values.

Calculates the product of values of all the input rasters for each cell. Tends towards small values, if even one of the inputs has a small value or if many inputs have intermediate values.

The GAMMA type is typically used to combine more basic data. When gamma is 1, the result is the same as fuzzy SUM. When it is 0, the result is the same as fuzzy PRODUCT. Values between 0 and 1 allow you to combine evidence to produce results between the two extremes established by fuzzy AND or Fuzzy OR.

Table 2. Parameters of the Fuzzy Overlay functions with a short explanation. Edited from http://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/fuzzy-overlay.htm.

Figure 4. The Fuzzy Membership tool. Specify the Fuzzy Membership type, Mid point , Spread and Hedge.

- Determination of the midpoint is critical for the success of the modelling. In the current version of the MPM online tool, there is no tool to study the distribution of the data e.g. as a histogram or descriptive statistics besides the mean. If the midpoint field in the Fuzzy Membership tool is left empty the tool uses mean of the input data as a mid point . In this case, the chosen extent is considered. It is an advisable practice to run a Fuzzy Membership tool first with an empty mid point field and keep notes of the used mid point which is reported in the 'Running model. '-window and updated back to tool if it was originally left empty. This way the user will know the mean value of the data and can then start to increase or decrease it manually for the following runs of the model.

- Connect the Fuzzy Membership output rasters to each other with Fuzzy Overlay tool. The assumption is that the user has scaled the inputs between 0 and 1 prior to combining them with a Fuzzy Overlay function.To specify the parameters the Fuzzy Membership tool can be opened by double clicking the Fuzzy Overlay rectangle. An additional window opens up to specify the parameters (Fig. 5).

- The Fuzzy Overlay types and their short definition is given in table 2.

- To view active parameters of the tool, hover the mouse over tool and the tooltip opens.

Returns the minimum value of all of the input evidence rasters for each cell.

Returns the maximum value of all of the input evidence rasters for each cell.

Calculates the product of unfavourabilities of all the input rasters and subtracts this from unity for each cell. Tends towards large values if even one of the inputs has a large value or if many inputs have intermediate values.

Calculates the product of values of all the input rasters for each cell. Tends towards small values, if even one of the inputs has a small value or if many inputs have intermediate values.

The GAMMA type is typically used to combine more basic data. When gamma is 1, the result is the same as fuzzy SUM. When it is 0, the result is the same as fuzzy PRODUCT. Values between 0 and 1 allow you to combine evidence to produce results between the two extremes established by fuzzy AND or Fuzzy OR.

Table 2. Parameters of the Fuzzy Overlay functions with a short explanation. Edited from http://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/fuzzy-overlay.htm.

Figure 5. The Fuzzy Overlay tool. Specify the Fuzzy Overlay type and the Gamma value (default 0.9).

Specifying the processing extent

- The default processing extent of the MPM online tool is Northern Finland.

- A smaller rectangle for the processing extent can be drawn on the map. In case of complex models this may be preferable to limit the processing time. The extent rectangle can be drawn using a rectangle tool (Fig. 5) under " ModelBuilder " located on the left hand side of the MPM online tool (Fig. 1). After selecting the tool, draw an area on the map by clicking in one corner of the extent with the left mouse button, keeping the mouse down and releasing it in the opposite corner of the extent.

Figure 6. The Model Builder Tool. Specify the processing extent with the rectangle tool and the known deposits and the modelled deposit type under "Select a layer for sample points".

- Select the modelled deposit type under "Select a layer for sample points". The selection of the deposit type is only made for model validation with receiver operating characteristics (ROC) curves and their under the area curve (AUC) value (see FUZZY LOGIC for explanation). The data is in vector point file format and is derived from the GTK "Mineral deposit database". Choose None if you do not want to validate your model with the ROC tool. Naturally, the accuracy of the model is validated based on this selection regardless of what raster inputs you have selected to the model.

- When the geoprocessing model in the Model Builder is ready, the processing extent is drawn on the map and the selected deposit type is selected, the model can the executed pressing the Run model button located in the MPM online tool (Fig. 6).

- The model is validated and the running order is determined by performing topological sort over tool nodes in the graph. This ensures the model is run in the correct order.

- When the model is running a "Running model. " information window is automatically opened on top of the browser.

- All input rasters in the model must be connected to a tool otherwise a warning will appear in the Running model. window and model will stop running.

- When running the model the Fuzzy tool flashes red. When the calculation is completed it turns green and plots the AUC box next to the Fuzzy Membership or Fuzzy Overlay rectangles.

- Raster analysis output layers will appear in the Model Builder when each step of the model is completed. A layer is generated for Fuzzy Memberships and Fuzzy Overlay outputs for viewing.

- When the model is running the model quality is being assessed for each input separately with ROC curves. The AUC of the ROC curve is reported in a box after each Fuzzy membership and Fuzzy Overlay step of the model. The ROC curve can be viewed by clicking at the AUC box appearing next to the Fuzzy tools in the Model Builder (Fig. 7).

- AUC box will appear green when AUC >0.5 and red when AUC <0.5 (Fig. 7).

- The Running model. information window will inform you when the model processing is completed. Press "Close" to exit the window.

Figure 7. An example fuzzy logic model made for orogenic gold deposits.

- A output layers will appear under a group layer in the ModelBuilder .

- You can remove the created model output layers from the ModelBuilder and map with the button Clear geoprocessing rasters located in the ModelBuilder . The model has to be run again in order to recreate the outputs.

- The rainbow colour palette (red-yellow-green-blue) with Percent Clip (1%) histogram stretching is used as a default. Unfortunately, in the current version of the MPM online tool the histogram stretching or classification of the colours cannot be performed by the user.

- When you rerun the model again the new output layers will appear under a new group layer. The new output layers in the Raster analysis layers have with the same layer names as in the previous runs of the model. In case you want to compare models you must be careful and keep your own notes how the versions of the model inputs and parameters differ in different model runs.

- New model button removes the current model from the MPM online tool and opens up a new empty Model builder window. The old model will not be saved. Do not press the button unless you want to create a completely new model from scratch.

Visual assessment of the model outputs

- The final outputs of the model can be viewed in the map and overlain by background data from GTK and other sources.

- The minimum and maximum values of the outputs can be seen under Layers> layer name> Selite .


Abstract

The development of spatial planning and management approaches is required to increase the space available for aquaculture production and to support the increasing global demand for food resources. During a European funded project, a large consultation exercise highlighted that stakeholder involvement is a necessity for successful planning and must be a continuous process as part of the development of a decision-making tool. In this study we present a decision support tool built on a web based dynamic interface to Geographic Information Systems which facilitates access to information related to site selection, environmental interactions and management in aquaculture. It is derived from the AkvaVis concept and uses interactive functions that instantly display the results of spatial parameters chosen by the user. We adapted the tool for use within four case studies which deal with very different scales of aquaculture and issues related to aquaculture in four different countries. The key strengths of our tools relate to their capacity to manage and display spatial data from different sources in a transparent way, the ability to use and display a series of built-in indicators, and the long-term development potential made possible by the maintenance strategy of the tools, services and data depository. Consultations and meetings provided an accurate view of stakeholder expectations as well as feedback on the tool development and applicability, therefore helping the tool to meet the prerequisite for operational decision-making tools.


Clients communicate to Workspace ONE UEM on behalf of the device. There are two primary management clients:

The clients serves their own, distinct purposes, and rely on different services to establish real-time communication with Workspace ONE UEM. The following table compares them in more detail.

  • Device communication
  • Device enrollment
  • Profile configuration using Microsoft CSPs
  • Software distribution metadata delivery using VMware CSPs
  • Profile configuration
  • Local policy enforcement
  • Sensors, Scripts, & Workflows
  • Baselines
  • Unified App Catalog
  • Hub Services
  • Product provisioning

Server Manager is installed by default with all editions of Windows Server 2012 R2 and Windows Server 2012. No additional hardware requirements exist for Server Manager.

Server Manager is installed by default with all editions of Windows Server 2012. Although you can use Server Manager to manage Server Core installation options of Windows Server 2012 and Windows Server 2008 R2 that are running on remote computers, Server Manager does not run directly on Server Core installation options.

To fully manage remote servers that are running Windows Server 2008 or Windows Server 2008 R2, install the following updates on the servers you want to manage, in the order shown.

To manage servers that are running Windows Server 2012, Windows Server 2008 R2, or Windows Server 2008 by using Server Manager in Windows Server 2012 R2, apply the following updates to the older operating systems.

Windows Management Framework 4.0. The Windows Management Framework 4.0 download package updates Windows Management Instrumentation (WMI) providers on Windows Server 2012, Windows Server 2008 R2, and Windows Server 2008. The updated WMI providers let Server Manager collect information about roles and features that are installed on the managed servers. Until the update is applied, servers that are running Windows Server 2012, Windows Server 2008 R2 or Windows Server 2008 have a manageability status of Not accessible.

The performance update associated with Knowledge Base article 2682011 allows Server Manager to collect performance data from Windows Server 2008 and Windows Server 2008 R2. This performance update is not necessary on servers that are running Windows Server 2012.

To manage servers that are running Windows Server 2008 R2 or Windows Server 2008, apply the following updates to the older operating systems.

Windows Management Framework 3.0 The Windows Management Framework 3.0 download package updates Windows Management Instrumentation (WMI) providers on Windows Server 2008 and Windows Server 2008 R2. The updated WMI providers let Server Manager collect information about roles and features that are installed on the managed servers. Until the update is applied, servers that are running Windows Server 2008 or Windows Server 2008 R2 have a manageability status of Not accessible – Verify earlier versions run Windows Management Framework 3.0.

The performance update associated with Knowledge Base article 2682011 allows Server Manager to collect performance data from Windows Server 2008 and Windows Server 2008 R2.

Server Manager runs in the Minimal Server Graphical Interface that is, when the Server Graphical Shell feature has been uninstalled. The Server Graphical Shell feature is installed by default on Windows Server 2012 R2 and Windows Server 2012. If you uninstall Server Graphical Shell, the Server Manager console runs, but some applications or tools available from the console are not available. Internet browsers cannot run without Server Graphical Shell, so webpages and applications such as HTML Help (The MMC F1 Help, for example) cannot be opened. You cannot open dialog boxes for configuring Windows automatic updating and feedback when Server Graphical Shell is not installed commands that open these dialog boxes in the Server Manager console are redirected to run sconfig.cmd.