TY - JOUR

T1 - Mapping groundwater potential zone in the subarnarekha basin, India, using a novel hybrid multi-criteria approach in Google earth Engine

AU - Singha, Chiranjit

AU - Swain, Kishore Chandra

AU - Pradhan, Biswajeet

AU - Rusia, Dinesh Kumar

AU - Moghimi, Armin

AU - Ranjgar, Babak

N1 - Funding information: The quantification and delineation of groundwater resources employing conventional techniques such as geological, geophysical, or hydrogeological methods are often labor-intensive, cost-ineffective, and time-consuming [16]. Therefore, recording and evaluating the outcomes of subsurface hydrological inquiries can provide a better alternative approach to traditional groundwater potential mapping. A cohesive method combining Remote sensing (RS) and Geographic information system (GIS) approach can serve as a superior decision support system for intelligently assessing Groundwater Potential (GWP), groundwater quality suitability, discharge, recharge, and storage mapping [17–21]. Mohammed et al. [22,23] integrated RS and GIS techniques with the Analytic hierarchy process (AHP) method for effectively evaluating potential groundwater recharge zones in the Iraqi Western desert region. Tamesgen et al. [24] presented the GWP analysis with nine geo-environmental parameters through Ethiopia's RS/GIS-based Multi-Criteria Decision Making (MCDM) approach. Kisiki et al. [25]used geospatial and RS data to define the groundwater recharge zones through the GWP evaluation. They then performed a sensitivity analysis to determine the impact of hydrologic and geological factors on their variations. The widely used multi-criteria-based decision support techniques for GWP mapping include AHP [22,26–28], Frequency ratio (FR) [29], Logistic regression (LR) [30], Fuzzy set [31], Quick unbiased efficient statistical tree (QUEST) [32], Weighted linear combination (WLC) [33], Evidential belief function (EBF) [34], Multi-influencing factor (MIF) [35], Shannon's entropy [36], TOPSIS [37], Dempster-Shafer model [38], Bayesian network model [39] etc. Causal relationships based on Fuzzy decision-making trial and evaluation laboratory (FDEMATEL) approaches have also been applied for soil erosion, flood, and landslide susceptibility mapping [40,41]. Such integrated methods have also been used for groundwater potential mapping, for instance the study by Echogdali [42] in the Akka Basin, Morocco.

PY - 2024/1/30

Y1 - 2024/1/30

N2 - Assessing groundwater potential for sustainable resource management is critically important. In addressing this concern, this study aims to advance the field by developing an innovative approach for Groundwater potential zone (GWPZ) mapping using advanced techniques, such as FuzzyAHP, FuzzyDEMATEL, and Logistic regression (LR) models. GWPZ was carried out by integrating various primary factors, such as hydrologic, soil permeability, morphometric, terrain distribution, and anthropogenic influences, incorporating twenty-seven individual criteria using multi-criteria decision models along with a hybrid approach for the Subarnarekha River basin, India, in Google earth engine (GEE). The predictive capability of the model was evaluated using a Multi-Collinearity test (VIF <10.0), followed by applying a random forest model, considering the weighted impact of the five primary factors. The hybrid model for GWPZ classification showed that 21.97 % (4256.3 km2) of the area exhibited very high potential, while 11.37 % (2202.1 km2) indicated very low potential for GW in this area. Validation of the groundwater level data from 72 observation wells, performed by the Area under receiver operating characteristic (AUROC) curve technique, yielded values ranging between 75 % and 78 % for different models, underscoring the robust predictability of GWPZ. The hybrid and LR-FuzzyAHP models demonstrated remarkable effectiveness in GWPZ mapping, indicating that the downstream and southern regions boast substantial groundwater potential attributed to alluvial soil and favorable recharge conditions. Conversely, the central part grapples with a scarcity of groundwater. It holds the potential to assist planners and managers in formulating strategies for managing groundwater levels and alleviating the impacts of future droughts.

AB - Assessing groundwater potential for sustainable resource management is critically important. In addressing this concern, this study aims to advance the field by developing an innovative approach for Groundwater potential zone (GWPZ) mapping using advanced techniques, such as FuzzyAHP, FuzzyDEMATEL, and Logistic regression (LR) models. GWPZ was carried out by integrating various primary factors, such as hydrologic, soil permeability, morphometric, terrain distribution, and anthropogenic influences, incorporating twenty-seven individual criteria using multi-criteria decision models along with a hybrid approach for the Subarnarekha River basin, India, in Google earth engine (GEE). The predictive capability of the model was evaluated using a Multi-Collinearity test (VIF <10.0), followed by applying a random forest model, considering the weighted impact of the five primary factors. The hybrid model for GWPZ classification showed that 21.97 % (4256.3 km2) of the area exhibited very high potential, while 11.37 % (2202.1 km2) indicated very low potential for GW in this area. Validation of the groundwater level data from 72 observation wells, performed by the Area under receiver operating characteristic (AUROC) curve technique, yielded values ranging between 75 % and 78 % for different models, underscoring the robust predictability of GWPZ. The hybrid and LR-FuzzyAHP models demonstrated remarkable effectiveness in GWPZ mapping, indicating that the downstream and southern regions boast substantial groundwater potential attributed to alluvial soil and favorable recharge conditions. Conversely, the central part grapples with a scarcity of groundwater. It holds the potential to assist planners and managers in formulating strategies for managing groundwater levels and alleviating the impacts of future droughts.

KW - FuzzyDEMATEL

KW - GWPZ

KW - Hydrologic

KW - Multi-collinearity

KW - Normalized difference vegetation index

KW - Random forest

UR - http://www.scopus.com/inward/record.url?scp=85182382987&partnerID=8YFLogxK

U2 - 10.1016/j.heliyon.2024.e24308

DO - 10.1016/j.heliyon.2024.e24308

M3 - Article

AN - SCOPUS:85182382987

VL - 10

JO - Heliyon

JF - Heliyon

SN - 2405-8440

IS - 2

M1 - e24308

ER -