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地下水水文地球化学行为对印度恒河三角洲全新世含水层地下水资源的影响:数据驱动算法的注入。

Effect of hydrogeochemical behavior on groundwater resources in Holocene aquifers of moribund Ganges Delta, India: Infusing data-driven algorithms.

机构信息

Department of Geography, The University of Burdwan, Bardhaman, West Bengal, 713104, India.

Department of Geography, The University of Burdwan, Bardhaman, West Bengal, 713104, India.

出版信息

Environ Pollut. 2022 Dec 1;314:120203. doi: 10.1016/j.envpol.2022.120203. Epub 2022 Sep 20.

Abstract

One of the fundamental sustainable development goals has been recognized as having access to clean water for drinking purposes. In the Anthropocene era, rapid urbanization put further stress on water resources, and associated groundwater contamination expanded into a significant global environmental issue. Natural arsenic and related water pollution have already caused a burden issue on groundwater vulnerability and corresponding health hazard in and around the Ganges delta. A field based hydrogeochemical analysis has been carried out in the elevated arsenic prone areas of moribund Ganges delta, West Bengal, a part of western Ganga- Brahmaputra delta (GBD). New data driven heuristic algorithms are rarely used in groundwater vulnerability studies, specifically not yet used in the elevated arsenic prone areas of Ganges delta, India. Therefore, in the current study, emphasis has been given on integration of heuristic algorithms and random forest (RF) i.e., "RF-particle swarm optimization (PSO)", "RF-grey wolf optimizer (GWO)" and "RF-grasshopper optimization algorithm (GOA)", to identify groundwater vulnerable zones on the basis of field based hydrogeochemical parameters. In addition, correspondence health hazard of this area was assessed through human health hazard index. The spatial distribution of groundwater vulnerability revealed that middle-eastern and north-western part of the study area covered by very high and high, whereas central, western and south-western part are covered by very low and low vulnerability zones in outcomes of all the applied models. The evaluation result indicates that RF-GOA (AUC = 0.911) model performed the best considering testing dataset, and thereafter RF-GWO, RF-PSO and RF with AUC value is 0.901, 0.892 and 0.812 respectively. Findings also revealed the groundwater in this study region is quite unfavorable for drinking and irrigation purposes. The suggested models demonstrate their usefulness in foretelling sustainable groundwater resource management in various deltaic regions of the world through taking appropriate measures by policy-makers.

摘要

其中一个基本的可持续发展目标是获得用于饮用的清洁水。在人类世时代,快速的城市化进程进一步给水资源带来了压力,与之相关的地下水污染问题扩大成为一个重大的全球性环境问题。自然砷及相关的水污染已经给恒河三角洲及其周边地区的地下水脆弱性和相应的健康危害带来了负担。在孟加拉国恒河三角洲垂死地区的高砷地区进行了基于实地的水文地球化学分析,该地区是西孟加拉邦西部恒河-布拉马普特拉河三角洲(GBD)的一部分。新的数据驱动启发式算法很少用于地下水脆弱性研究,特别是在印度恒河三角洲的高砷地区尚未使用。因此,在当前的研究中,重点是整合启发式算法和随机森林(RF),即“RF-粒子群优化(PSO)”、“RF-灰狼优化器(GWO)”和“RF-蚱蜢优化算法(GOA)”,根据实地水文地球化学参数识别地下水脆弱带。此外,还通过人体健康危害指数评估了该地区的对应健康危害。地下水脆弱性的空间分布表明,研究区中东部和西北部地区覆盖着极高和高脆弱性区,而中部、西部和西南部地区则覆盖着极低和低脆弱性区,这是所有应用模型的结果。评估结果表明,考虑到测试数据集,RF-GOA(AUC=0.911)模型表现最佳,其次是 RF-GWO、RF-PSO 和 RF,其 AUC 值分别为 0.901、0.892 和 0.812。研究结果还表明,该研究区域的地下水不太适合饮用和灌溉。通过决策者采取适当措施,所建议的模型展示了它们在预测世界上各种三角洲地区可持续地下水资源管理方面的有用性。

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