Tonle Sap Decision Support System (Tonle Sap DSS) was developed to aid decision makers to identify target communities for scaling out promising water management interventions in the Tonle Sap region in Cambodia. It is a GIS based decision support tool that combines multi-criteria analysis and optimization techniques, and generates a spatial output at the village scale. The output indicates the relative suitability of multiple villages for receiving and accommodating a given water management intervention.
The DSS has two steps. An initial screening step allows the user choose a subset of available decision options adhering to a number of biophysical and socio-economic constraints. An outranking step that follows makes pair-wise comparisons within that subset of decision options to return a maximum suitability score indicating which villages are better suited than others. Using georeferenced data from secondary sources including the Cambodian Commune database, ID Poor database, and Landsat and MODIS databases, the DSS models the constraints to build a relationship between the current capacity of the intervention (supply), potential for its development (demand), and the favorability of the context. It is a simple, yet highly interactive DSS that permits the user to define the problem situation and consider alternative blueprints of targeting. In other words the model does not use built-in assumptions on how the constraints would behave, but rather permit the user to define these relationships.
Tonle Sap DSS was developed as a part of the CGIAR research program on Aquatic Agricultural Systems (AAS). The AAS was commissioned to help the resource poor people living in aquatic agricultural systems to harness the system's development potential. In Cambodia, the program worked in 12 villages in the Tonle Sap region and identified three water management interventions that could potentially improve the livelihoods and living standards of its people. These interventions, which are already being piloted in three focal villages include, improving availability and access to potable water, fish cage culture, and dry season irrigation water. The DSS models the potential for scaling out these three interventions
There are 1546 villages in the Tonle Sap study area. From this, based on a decision situation that you define, the Tonle Sap DSS will select a set of villages that is better suited to receive and accommodate a given water management intervention.
First, go to the maps tab and explore the different map layers to understand and acquaint yourself with the various environmental, social, and economic conditions in the Tonle Sap area.
Then, select an intervention you wish to target or scale-out.
Next, you can start building a decision situation, by setting up constraints against which the list of villages will be screened. There are three sets of criteria, listed in the three separate tabs. These include (a) bio-geographic suitability (b) socio-economic suitability and (c) extent of agricultural activity. You can choose up to a minimum of one and a maximum of five criteria. Define the constraints by specifying inequalities for each criterion. Click the submit button.
Then, select the outranking variables, by which you wish to rank the set of villages that you have already screened. There are three sets of outranking variables for each intervention. Select three outranking variables one from each tab. You need to specify two inputs for each criterion: (a) the order of outranking and (b) the weight. To define an order of outranking of your choice, specify whether you desire more of the variable in the decision situation or less of the variable. Input the weights in the boxes given by specifying the importance of the variables in an ascending order from 1 to 3, assigning 3 to the most important variable and 1 to the least important variable. Variables considered to be of equal importance should be assigned the same value. Click the submit button.
Based on your choice of decision situation, the Tonle Sap DSS will generate a map of the villages that meet the screening constraints you set and ranked by the outranking variables you chose.