Multi-Criteria Decision Making (MCDM)

"A branch of operational research dealing with finding optimal results in complex scenarios including various indicators, conflicting objectives and criteria." (Kumar, 2017)

Sources

Kumar, Abhishek, Bikash Sah, Arvind R. Singh, Yan Deng, Xiangning He, Praveen Kumar, and R. C. Bansal. "A review of multi criteria decision making (MCDM) towards sustainable renewable energy development." Renewable and Sustainable Energy Reviews 69 (2017): 596-609.

Related Content

Local Perspectives Case Studies

Hydrometeorological disasters in the Indian Himalayas

Flash flood in Uttarakhand, India
Hydrometeorological disasters (HMDs) in the Hindu Kush Himalayan (HKH) area have led to multiple water-related issues that resulted from extreme rainfall, glacial melt, and changing river flows, all of which are made worse by climate change and land use changes. Accurate warnings of these disasters are difficult due to sparse gauging and rugged topography in the Garhwal Himalaya region, which increases the likelihood of disasters during the monsoon. The same region experiences water shortage and drought especially during non-monsoon periods. The use of wide coverage remote sensing data from the study region as well as from neighboring countries with access to space-based data can play a significant role in the monitoring and analysing of these challenges. This study applies spatiotemporal clustering and multi-criteria decision-making (MCDM) to map high-risk zones, which will allow policymakers to reinforce infrastructure providing disaster resilience. There is a need for a solution that uses multi-criteria decision making (MCDM) and spatiotemporal clustering to map areas in Uttarakhand, Himalaya, that are prone to disasters with the help of satellite-based data. To determine which tehsils (smaller administrative units) are vulnerable, it is suggested to examine more than 150 years of recorded disaster data with location and fatalities. Further vulnerable regions can be mapped using high-resolution satellite data (procured through Sentinel, Landsat, Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and Tropical Rainfall Measuring Mission (TRMM)) and analysed in the QGIS platform. This solution could use spatiotemporal clustering and MCDM to map high-risk zones, which will allow policymakers to reinforce infrastructure providing disaster resilience. Data of the Garhwal Himalayan region (India), which lies in the Hindu Kush Himalayan (HKH) region are needed. The topography of the HKH region is almost the same over eight countries, and all bear similar kinds of disasters and climate patterns. The Garhwal region occupies about 64 per cent of the area of the Uttarakhand state and is also the origin of the river Ganga.

Space-based Solution

Addressed challenge(s)

Hydrometeorological disasters in the Indian Himalayas

Collaborating actors (stakeholders, professionals, young professionals or Indigenous voices)
Suggested solution

The historical disasters of the study region, the Garhwal Himalaya, were collected, and the types of hydrometeorological disasters (HMD) were tabulated with location, attribute, morbidity, and extent from 1803 to 2025. The Garhwal region has been divided into 58 tehsils (sub-administrative regions). For analysing past HMDs and to map Multi-Hazard Susceptibility Zonation on the tehsil level, QGIS, Google Earth Engine, satellite data, k-means clustering, and AHP techniques were used.

Requirements

Data

  • Survey of India map of the study area
  • Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM)
  • Tropical Rainfall Measuring Mission (TRMM) rainfall data
  • Sentinel-2 Land Use / Land Cover (LULC) data
  • Global Land Ice Measurements from Space (GLIMS) Glacier data
  • National Bureau of Soil Survey & Land Use Planning (NBSS&LUP) Soil data
  • Disaster data from Emergency Events Database (EM-DAT)
  • National Disaster Management Authority (NDMA)
  • Various research publications
  • Along with regional newspapers

Software

  • QGIS
  • Google Earth Engine (GEE)

Steps to a solution

  1. Study Area

The Garhwal region is spread over approximately 32,366 square kilometres in northwestern Uttarakhand and comprises 58 sub-administrative divisions (tehsils). This region is a major part of the Indian Himalayan Region (IHR) and has steep slopes, rugged terrain, and a geologically fragile structure, and hence is highly vulnerable to natural hazards. Though associated with steep topography, its intense monsoonal rainfall, changing land use patterns, and glacial influence all make the region highly vulnerable to hydrometeorological disasters (HMDs) such as floods, flash floods, landslides, GLOFs, cloud bursts, and avalanches.

Google Earth satellite image of the Garhwal region in India
Figure 1. Google Earth satellite image of the Garhwal region

 

Digital elevation model of the Garhwal region
Figure 2.  DEM of Garhwal region

 

Spatial distribution of hydrometeorological disasters and facilities in the Garhwal Himalayas
Figure 3. Spatial distribution of HMDs and fatalities in the Garhwal Himalayas (1803–2025)

 

  1. Collecting and processing  

Historical HMD data for the Garhwal region (1803–2025) have been collected from a variety of sources, including EM-DAT, scientific publications, NDMA, SDMA, and regional media reports.

Table 1. Geospatial Datasets Used in the Study
S.No.Dataset / LayerSource / MethodResolution / FormatYear / Period
1Study Area ShapefileSurvey of India / Custom DigitisationVector (Shapefile)Latest Available
2Digital Elevation Model (DEM)NASADEM30 m (Raster)2020
3Slope and ElevationDerived from NASADEM using QGIS30 m (Raster)2020 (Processed)
4Monsoon RainfallTRMM via GEEMonthly, ~25 km (Raster)1998-2015
5Land Use / Land Cover (LULC)ESA WorldCover (Sentinel-2)10 m (Raster)2021
6Glacier CoverGLIMS / ESA~30 m (Raster)Latest Available
7Proximity to RiversHydroSHEDSVariable (Rasterised)Processed Layer
8Soil Erosion ClassNBSS&LUP Database, IndiaVector -> Raster ConversionLatest Available

 

  1. How Thematic Layer Preparation Works

Seven thematic layers were created for the Garhwal region using satellite remote sensing data in QGIS and the GEE environment:

  1. Slope
  2. Elevation
  3. Rainfall
  4. Land Use/Land Cover (LULC)
  5. Soil Erosion
  6. The region’s closeness to rivers
  7. Glacier Proximity

The thematic layers were created using the data sourced from Table 1. Thematic layers were brought to the same scale (1–5) and brought together using AHP to develop a single risk zonation map.

The k-means clustering is done on the QGIS 3.42.3 platform using K-Means Clustering ABC (Attribute-based clustering) tab in the processing toolbox. The attributes were selected like location, elevation, and impact severity.

  1. Application of AHP

To evaluate HMD susceptibility using AHP, the main influencing factors were selected: slope, elevation, rainfall, LULC, soil erosion, rivers and glacier proximity. To create these layers, data from DEM for slope and elevation, image data from satellites for LULC, and hydrological data are used. Based on AHP, a table is filled, with one factor compared to another according to Saaty’s 1–9 scale to decide their relative weight. Weights are calculated with an eigenvector analysis, and a small consistency ratio (less than 0.1) indicates sensible conclusions. Finally, using an AHP weighted overlay in GIS, all the relevant layers are combined, and the outcome is a map showing where HMD susceptibility is highest.

Methodology flowchart
Figure 4. Methodology Flowchart

 

Results

  • Most (77.6 per cent) HMDs happened during the Monsoon season, followed by pre-monsoon (14.3 per cent), Winter season (6.1 per cent), and the post-monsoon season (2 per cent).   
  • The K-means clustering of disaster events in the Garhwal Himalayas yielded the clusters-based partitioning them based on shared characteristics (e.g., elevation, impact severity, location). 
  • The multi-hazard zonation using the AHP system shows that the north eastern or north tehsils like Joshimath and Chamoli have very high levels of risk, while places like Haridwar and Roorkee in the south have much lower risks.
AHP-based multi-hazard risk zonation in the Garhwal Himalayas
Figure 5. AHP-Based Multi-Hazard Risk Zonation in the Garhwal Himalayas

 

Future work  

  • Access and incorporation of socio-economic and infrastructure vulnerability cum exposure data for risk zonation 
  • Combining GIS outputs with participatory approaches to validate and refine vulnerability maps on the ground 
  • Make the methodology suitable and easily workable for the entire Himalayan Region to strengthen resilience against disasters 
  • Future climatic scenario along with ML to recognize and forecast disaster patterns  
  • Using space-based techniques for ecosystem-based disaster reduction
Related space-based solutions
Keywords (for the solution)
Climate Zone (addressed by the solution)
Habitat (addressed by the solution)
Region/Country (the solution was designed for, if any)
Relevant SDGs