Multi-criteria decision making and Dempster-Shafer model–based delineation of groundwater prospect zones from a semi-arid environment

Abstract

The present study illustrates the delineation of the groundwater potential zones in one of the most critical and drought affected areas under Bundelkhand region of Uttar Pradesh. Hydrological evaluations were carried out in district Mahoba using GIS tools and remote sensing data which ultimately yielded several thematic maps, such as lineament density, land use/land cover, drainage density, lithology, slope, geomorphology, wetness index (WTI), altitude and soil. CartoDEM data which have spatial resolution of 30m i.e. equivalent to one arc second were used to create digital elevation model, drainage density, altitude, WTI and slope. The thematic layers were assigned relative weightages as per their groundwater potential prospects under multi-criteria decision making (MCDM) method through analytical hierarchy process (AHP). To recognize the groundwater potential zone, weighted overlay analysis was performed using ArcMap software. Additionally, for testing of the Dempster-Shafer model, 16 borewells in high potential areas have been selected. Based on the probability of the groundwater occurrence, the belief factor was equated. Further combining the weighted layers, groundwater potential zones were obtained. The groundwater potential maps illustrate five zones having different potential in the Mahoba district. According to the AHP model the north-west side of the study area is characterized with very good potential zones whereas the north-east and southeast region constitute medium and poor groundwater potential zones respectively. It reflects that more than 50% of the area is having medium groundwater potential while 30 percent of the area falls under low potential zone. 10% of the study area falls under very good groundwater potential zones. According to the DS model, very high groundwater zones constitute only 7% and the remaining area falls under poor potential. Overall accuracy of the DS model was higher than AHP model.

Assessment of Groundwater Potential Zones by Integrating Hydrogeological Data, Geographic Information Systems, Remote Sensing, and Analytical Hierarchical Process Techniques in the Jinan Karst Spring Basin of China

“…This factor holds significance in evaluating groundwater potential zones and has been established as an indicator of favourable groundwater occurrence. Nevertheless, because of its susceptibility to terrain wetness and topographic gradient (slope), which could result in redundancy within multi-criteria decision making, this thematic layer was excluded as a decisive factor in the AHP model [51]. The TWI is frequently utilised to identify potential groundwater zones by depicting wetness patterns [46].…”

Enhancing Urban Sustainability by Integrating MCDM and Model of GIS Spatial Analysis in Al-Nasiriyah Heritage Center‎ Development

“…Analytical Hierarchy Process (AHP): is one of the techniques to calculate weight, in the context of the decisionmaking process [50].…”

A Clustering Multi-Criteria Decision-Making Method for Large-Scale Discrete and Continuous Uncertain Evaluation

“…A considerable number of traditional MCDM technologies can do this, such as AHP [19,20], TOPSIS [21], VIKOR [18], BWM [23], ELECTRE [22], MARCOS, and ARAS [7,13]. In addition to these traditional MCDM techniques, the D-S theory can also be used to select the optimal DA by treating each criterion as a mass function whose frame of discernment is the set of all DAs and then combining the mass functions of all the criteria by Dempster's combination rule [25,26]. Furthermore, Dempster's combination rule satisfies the commutative and associative laws, making it very efficient in fusing information from multiple sources [11,12].…”

“…AHP follows the way of thinking of decomposition, comparison, judgment, and synthesis for decision making and establishes a hierarchical structure model, with the top layer as the target layer, the middle layer as the criterion layer, and the bottom layer as the decision alternative layer, which can clearly show the relationships between each layer, each criterion, and each decision alternative [24]. In addition, similarly to the hierarchical structure of the AHP, the D-S theory can also accomplish this task by treating each criterion as a mass function generated by the evaluation of DAs, and the synthesized aggregated evaluation is operationalized by Dempster's combination rule [25,26]. As the core of D-S theory, Dempster's combination rule has excellent properties, such as the commutative and associative laws, which enable it to fuse multi-source information efficiently [25,27].…”