Ten Project Ideas where you can apply both MCDM and GIS

Project Ideas with MCDM and GIS

Nov 27, 2022

The following ideas will also be featured in the yet-to-publish ebook: “50 Project Ideas on MCDM and GIS” (PREORDER)

This post tries to provide ten numbers of original and unpublished project ideas that can be accomplished with the help of Multi-Criteria Decision Making (MCDM) methods and Geographical Information System(GIS).

Idea 1: Vulnerability Analysis of Water Resources at Periurban Watersheds in face of Climatic Abnormalities with the help of Vulnerability Index and GIS.

The uncontrolled utilization of water resources and the burgeoning population, and their demand for luxury have induced abnormal warming of the global climate. As a result, the regular pattern of climate has been compromised which has multifaceted direct and passive impacts on the water resources of the river basins. Although there is a noticeable number of studies which has tried to highlight the vulnerabilities of the watersheds towards this abnormal climatic pattern but impact analysis of climatic ambiguities on the water resources of peri-urban watersheds has been rarely attempted. The periurban watersheds can be segregated as unique watersheds which flow through a periurban landscape. Such basins do not face the urbanization impacts which are encountered by the urban watersheds but these watersheds also do not have the healthiness of rural watersheds either. That is why policymakers and planners of the periurban watershed are often confused with the optimal utilization of the water resources where the intensity of water demand is not as stiff as in urban basins but higher than in its rural counterpart. That is why the Vulnerability Index which was developed for urban and rural watersheds can not be implemented here. The present study will try to develop a Vulnerability Index for peri-urban watersheds with the help of MCDM and represent the vulnerability of peri-urban basins with the help of GIS.

Idea 2: Real-time monitoring of the water quality of Wetlands with the help of the Wetland Pollution Index(WPI) and a Geospatial Decision Support System(GDSS)

Wetlands in any urban conglomerate are slowly but steadily deteriorating and diminishing due to rapid urbanization and uncontrolled utilization of surface water bodies. The present study will try to use various MCDM techniques to find the optimal features for the development of a Wetland Pollution Index. Such parameters can be utilized as alternatives and the scope and capacity of polluting the wetland can be considered as Criteria. The most important eight parameters can be selected and utilized for developing the WPI. Once WPI is prepared it can be used along with the GIS map to represent the pollution content of all the wetlands. The GIS map can be interlinked with the instruments used for in situ monitoring of the wetlands in real-time.

Idea 3: Location Selection for installation of a New Water Treatment Plant in the Border Area

Due to the remote locations of the Army Camps, the supply of drinking water is very limited. That is why there is an acute need for Water Treatment Plants in proximity to the camp. However, there are various conflicting and non-conflicting factors that must be considered before a location can be identified. Again not all the factors will be equally significant. Each factor will have its own importance. Otherwise, for some factors, the decision maker will give over importance, and for others under importance. Here also MCDM can be utilized effectively to objectively define the weightage reflecting the significance of each of the factors. A Location Selection Index can be developed with the help of the weightage and GIS maps can be generated based on the index values of each of the locations in the border area. The GIS map will enable decision-makers to identify the most suitable area for the installation of Water Treatment Plants.

Idea 4: Vulnerability analysis of pineapple industries due to climate change with the help of MCDM techniques and GIS Interface

Justification :

The pineapple is a tropical plant with edible fruit, and it is the most economically important plant in the Bromeliaceae family. Pineapple cultivation is restricted to hilly regions in northeastern India and coastal regions of peninsular India with high rainfall and humidity. It is also commercially grown in the interior plains with medium rainfall and supplementary irrigation. Pineapple is primarily grown under rainfed conditions. Supplemental irrigation aids in the production of large fruits in areas with abundant rainfall. Irrigation also helps to establish an off-season planting to maintain its year-round production. In case of scanty rainfall and hot weather, irrigation may be provided once in 20-25 days. The success of Pineapple cultivation depends on various climatic parameters as well as soil characteristics and irrigation requirements. Now due to global warming, the regular pattern of climate is changing and the occurrence of abnormal events is rising. As a result due to this aberration in the regular pattern of climate, pineapple productivity in many locations is getting vulnerable. That is why there is a need to analyze the impact of climatic abnormalities on the productivity of pineapples in different locations of the World.

Proposed Objective

There are various factors on which growth and harvesting of the crop will vary.No parameter is equally important. The significance varies with parameters. As a result, the climate change impacts are vaguely mensurable. The present study aims to introduce a Vulnerability Indeisces which will cognitively measure the impact of climate change on pineapple productivity and consequently the EBITDA of pineapple industries will be compared to identify the vulnerability with time. In this aspect, MCDM techniques like Analytical Hierarchy Process(AHP),MERCK etc can be used to determine the weightage of importance for each of the parameters, and the same weightage can be used to develop a weighted index as a function of the characteristics of the parameters in the industry and the weightage of the importance of that factor.

Idea 5. Modification of the Standard Precipitation Index (SPI) for Identification of Drought with the help of AHP and ANP MCDM method

SPI shows the actual precipitation compared to the probability of precipitation for various time frames. It is useful for both short and long-duration hydrological applications. For each month drought is identified based on the intensity of precipitation and is demarcated by a start and end time. "A drought event occurs any time the SPI is continuously negative and reaches an intensity of -1.0 or less. The event ends when the SPI becomes positive." The rainfall intensity is a function of the return period of the rainfall and the duration of the rainfall. It also has four constants that are required to estimate the rainfall intensity and depend on the watershed characteristics. But if MCDM is used where return period and time of durations is used as criteria and the four constants as alternatives with a goal of decision making as intensity of rainfall then an objective methodology can be proposed to determine the values of the constants. Currently, the values of the four constants are determined empirically as a result the equation becomes highly sensitive to location. After the weightage is determined and intensity is calculated GIS can be used for the spatial representation of SPI.

Idea 6: Development of the Modified Palmer Drought Severity Index (PDSI) with MERCK and DEMATEL method

This index has been used the longest for monitoring drought. The PDSI considers precipitation, temperature, and soil moisture to determine drought. However, this index is not suited for mountainous regions and areas with frequent extreme events. Palmer values may lag emerging droughts by several months. It has no defined start and ends time. Here Precipitation, Temperature, and Soil Moisture can be treated as criteria and the MERCK method or DEMATEL method can be used to determine their significance. This weightage of significance can be used along with the magnitude of the three parameters of PDSI to estimate the PDSI more objectively. After the determination of MPDSI, GIS maps can be generated for the representation of the spatial variation of MPDSI in a certain watershed or multiple watersheds.

Idea 7: Modification of Crop Moisture Index (CMI) with the help of MCDM and GIStechniques

CMIS uses soil moisture to identify droughts.CMI was designed to evaluate short-term moisture conditions across major crop-producing regions. Because it is designed to monitor short-term moisture conditions affecting a developing crop, the CMI is not a good long-term drought monitoring tool. The disadvantage of CMI is it may consider the soil moisture after an occasional rainfall during a drought and indicate a non-drought condition while the long-term drought at that location still persists. That is why the objective of the present idea is to utilize image processing to identify soil moisture for a specific duration of t and one, two, and three days before t and made the CMI a function of these four parameters. The occurrence and duration of rainfall can be taken as criteria and the four parameters as alternatives then AHP or Analytical Network Process(ANP) or MACBETH can be used to determine the significance weightage of the four alternatives. The objective is the nearest event of rainfall will have minimum significance to CMI. The modified CMI will be a function of the product of both weightage and magnitude of rainfall at the present day and one, two, and three days before the current day of rainfall. Now this Modified CMI can be used to generate choropleth maps showing the magnitude of CMI in different parts of the watershed.

Idea.8: Development of Geographically Distributed and Cognitive Integrated Drought Index (IDI) with the help of MCDM methods and GIS frameworks

IDI combines the response of meteorological, hydrological, and agricultural droughts and accounts for groundwater storage. It integrates the 12-month Standardized Precipitation Index (SPI), 4-month Standardized Runoff Index (SRI), 1-month Standardized Soil Moisture Index (SSI), and 1-month Standardized Groundwater Index (SGI) to develop IDI. Hydrologic variables like total runoff, soil moisture, and groundwater are the input parameters of IDI. It can avoid the uncertainty due to rainfall data and provide a comprehensive picture of all three types of drought. However, the requirement of calculating so many indices can create data inconsistency as groundwater data is inaccessible in many places of the World. As a result, the present study will try to determine the weightage of significance for the hydrological variables(Alternatives) as mentioned previously with respect to accessibility, availability, and mensurability(Criteria) of the parameters for the selected location with the help of MCDM techniques. After this step, the weightage of the parameters and the magnitude of the parameters will be used to determine the SPI, SRI, SSI, and SGI. As a next step separate layer of the map will be created for each of the index and aggregation layers representing the IDI will be generated with the help of GIS to find the location-wise IDI values in a Watershed.

Idea 9: Development of Geospatially Sensitive Aridity Anomaly Index (AAI) for Long Term Drought Detection

What is AAI?

A real-time drought index developed by Indian Meteorological Department(IMD) considers water balance to identify droughts. "The Aridity Index (AI) is computed for weekly or two-weekly periods. For each period, the actual aridity for the period is compared to the normal aridity for that period. Negative values indicate a surplus of moisture while positive values indicate moisture stress."Actual evapotranspiration and calculated potential evapotranspiration are the input parameters.AAI can identify the agriculture drought, especially in the tropics where defined wet and dry seasons are part of the climate regime. The winter and summer cropping seasons can be evaluated using this method.

Justification for the present objective :

Although the calculations are simple, based on the comparison of actual and normal conditions. and have a weekly time step, but it is applicable only for the identification of agriculture droughts and short-term events.

Objective :

The avoidance of the restriction in AAI for “short-time events” was attempted with the help of MCDM and GIS. At first separate layers are created representing normal aridity for the current week and the previous four weeks with the help of GIS.

Then the weightage of significance for each layer will be determined with the help of MCDM where the four layers will be treated as alternatives and duration from the current week and potential evapotranspiration of the current week as criteria. The weightage of the significance of the four layers and calculated potential evapotranspiration can now be used as input parameters of the index. This modification will help the index to be used for long-term drought detection.

Idea 10: Development of an Integrated Drought Detection Index

The detection of drought events is not as easy as is for floods. The moment the level of water moves beyond a certain level, the occurrence of flood will be detected. But in the case of Drought, there is no such definition. Here the duration of the condition when moisture in soil moves below a certain percentage is considered for the detection of drought. As a result, there are various indices available for drought detection like SPI, PDSI, CMI, IDI, AAI, etc. For all the drought detection index duration of detection is important. For example, AAI and CMI are developed for short-term use and SPI, PDSI, and IDI can be used long-term but they have other limitations, like; IDI is highly data dependent, PDSI is not suitable for mountainous regions and regions with high extreme events. So to overcome the limitations of each index and to use the strength of the indicators an Integrated Drought Detection Index is developed from the SPI, PDSI, CMI, IDI, and AAI.

If the limitation of the indexes is observed then it can be seen that mainly categorized into four distinct categories: short-term duration, lack of data availability, consideration of multiple indexes, and others. In this group of other drawbacks like "presence of soil moisture due to sudden rainfall, characteristics of the area of interest, frequency of climatic events etc. can be categorized. If all these four groups of limitations are considered as criteria and all the indexes as alternatives then MCDM s can be implemented to find the weightage of the significance of each index. This weightage will change with changes in the study area. With the help of the weightage of significance for each index, the better index for the selected study area can be easily identified which can be used for drought detections for that region in the most acceptable manner. Of course, more indexes can be added to the list of alternatives and advanced methods like DEMATEL or MERCK can be used to find the weightage of significance for each of the criteria,i.e., the different types of limitations of the index considered in the study.

HydroGeek