Singha Chiranjit, Swain Kishore Chandra, Pradhan Biswajeet, Rusia Dinesh Kumar, Moghimi Armin, Ranjgar Babak
Department of Agricultural Engineering, Institute of Agriculture, Visva-Bharati (A Central University), Sriniketan, 731236, West Bengal, India.
Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia.
Heliyon. 2024 Jan 7;10(2):e24308. doi: 10.1016/j.heliyon.2024.e24308. eCollection 2024 Jan 30.
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 km) of the area exhibited very high potential, while 11.37 % (2202.1 km) 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.
评估地下水潜力以实现可持续资源管理至关重要。为解决这一问题，本研究旨在通过开发一种创新方法来推进该领域的发展，该方法利用模糊层次分析法（FuzzyAHP）、模糊决策试验和评价实验室法（FuzzyDEMATEL）以及逻辑回归（LR）模型等先进技术进行地下水潜力区（GWPZ）制图。通过整合各种主要因素，如水文、土壤渗透性、形态测量、地形分布和人为影响，在谷歌地球引擎（GEE）中采用多准则决策模型并结合混合方法，纳入二十七个单独标准，对印度苏巴纳雷卡河流域进行了GWPZ分析。使用多重共线性检验（VIF<10.0）评估模型的预测能力，随后应用随机森林模型，考虑五个主要因素的加权影响。GWPZ分类的混合模型显示，该区域21.97%（4256.3平方公里）的面积具有非常高的潜力，而11.37%（2202.1平方公里）的面积显示该区域的地下水潜力非常低。通过接受者操作特征（AUROC）曲线技术对72口观测井的地下水位数据进行验证，不同模型的值在75%至78%之间，突出了GWPZ的强大预测能力。混合模型和LR - FuzzyAHP模型在GWPZ制图中显示出显著效果，表明下游和南部地区由于冲积土和有利的补给条件而拥有大量地下水潜力。相反，中部地区面临地下水短缺问题。它有潜力协助规划者和管理者制定管理地下水位和减轻未来干旱影响的策略。
