Department of Geography, The University of Burdwan, Purba Bardhaman, West Bengal, 713104, India.
Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh.
Sci Rep. 2024 Jan 13;14(1):1265. doi: 10.1038/s41598-024-51917-8.
Determining the degree of high groundwater arsenic (As) and fluoride (F) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination.
确定高地下水砷（As）和氟（F）风险的程度对于成功的地下水管理和保护公众健康至关重要，因为地下水的污染升高对环境和人类健康构成了风险。事实上，有几个非点污染源污染了恒河三角洲的多层含水层的地下水。本研究使用逻辑回归（LR）、随机森林（RF）和人工神经网络（ANN）机器学习算法来评估恒河三角洲全新世多层含水层的地下水脆弱性，该地区是印度-孟加拉地区的一部分。为了建模目的使用了 15 个水化学数据，并进行了复杂的统计测试，以检查数据集之间的依赖关系。ANN 在验证数据集中的 AUC 为 0.902，表现最佳，并据此编制了地下水脆弱性图。脆弱性图的空间分布表明，东部以及一些孤立的东南部和中部中部地区在 As 和 F 浓度方面非常脆弱。总体预测表明，恒河三角洲的 29%的面积非常容易受到 As 和 F 含量的影响。最后，本研究讨论了全球主要的污染类别、不断上升的安全问题以及与地下水质量有关的问题。因此，必须显著改善地下水质量监测，以成功检测和减少过去、现在和未来污染对地下水的危害。
