基于遗传算法和机器学习的裂缝性含水层时空地下水潜力分区研究

IF 5 2区 地球科学 Q1 WATER RESOURCES
Prashant Parasar , Poonam Moral , Aman Srivastava , Akhouri Pramod Krishna , Sayantan Majumdar , Rajarshi Bhattacharjee , Arun Partap Mishra , Debjani Mustafi , Virendra Singh Rathore , Richa Sharma , Abhijit Mustafi
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引用次数: 0

摘要

在贾坎德邦断裂的硬岩含水层中,地下水的过度开采威胁着家庭、农业和城市需求不断增长的可持续性。本研究开发了一个综合框架,结合遗传算法(GA)优化聚类、随机森林(RF)回归和梯度提升(GB)分类,利用103口监测井和多个水文地质、地形和遥感变量,绘制了印度贾坎德邦(2013-2023)地下水潜势带(GWPZs)。采用遗传算法对水文地层聚类进行优化。Mann-Kendall (MK)检验评估了地下水的时间趋势,RF回归预测了未监测站点的地下水深度,并采用GB分类进行空间制图。使用局部可解释模型不可知论解释(LIME)提高了模型可解释性。该框架确定了三个gwpz(高、中、低),并通过强聚类指数(Silhouette = 0.90, Dunn = 0.94)进行了验证。MK分析显示,所有集群的地下水都明显枯竭(Z = -2.66至- 1.47,p <; 0.05)。RF回归具有较高的预测精度(R2≈0.91,WI = 0.89, PBIAS = 0.25),突出曲率和纹理接近度是主要因素。GB分级的f1评分为95.56 %。空间上,高潜力区集中在西兴、东兴和古姆拉,而吉里迪、帕库尔和加尔瓦则表现为低潜力区。这些发现为贾坎德邦2025年地下水法案提供了科学支持,并证明了该框架可转移到其他硬岩和数据稀缺的含水层,如巴西地盾和非洲克拉通。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating genetic algorithms and machine learning for spatiotemporal groundwater potential zoning in fractured aquifers

Study region

Groundwater overexploitation in Jharkhand’s fractured hard-rock aquifers threatens sustainability amid rising domestic, agricultural, and urban demands.

Study focus

This study develops an integrated framework that combines Genetic Algorithm (GA)-optimized clustering, Random Forest (RF) regression, and Gradient Boosting (GB) classification to map Groundwater Potential Zones (GWPZs) in the Jharkhand state of India (2013–2023) using 103 monitoring wells and multiple hydrogeological, topographic, and remote-sensing variables. GA was applied to optimize hydrostratigraphic clustering. The Mann-Kendall (MK) test assessed temporal groundwater trends, the RF regression predicted groundwater depth at unmonitored sites, and the GB classification was implemented for spatial mapping. Model interpretability was boosted using Local Interpretable Model-Agnostic Explanations (LIME).

New hydrological insights for the region

The framework identified three GWPZs (high, medium, and low), validated by strong clustering indices (Silhouette = 0.90, Dunn = 0.94). MK analysis revealed significant groundwater depletion across all clusters (Z = -2.66 to −1.47, p < 0.05). RF regression achieved high predictive accuracy (R2 ≈ 0.91, WI = 0.89, PBIAS = 0.25), highlighting curvature and lineament proximity as dominant factors. GB classification yielded an F1-score of 95.56 %. Spatially, high-potential zones were concentrated in West Singhbhum, East Singhbhum, and Gumla, while Giridih, Pakur, and Garhwa exhibited low potential with aquifer depletion. These findings provide scientific support for Jharkhand’s 2025 Groundwater Act and demonstrate the transferability of the framework to other hard-rock and data-scarce aquifers like the Brazilian Shield and African cratons.
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来源期刊
Journal of Hydrology-Regional Studies
Journal of Hydrology-Regional Studies Earth and Planetary Sciences-Earth and Planetary Sciences (miscellaneous)
CiteScore
6.70
自引率
8.50%
发文量
284
审稿时长
60 days
期刊介绍: Journal of Hydrology: Regional Studies publishes original research papers enhancing the science of hydrology and aiming at region-specific problems, past and future conditions, analysis, review and solutions. The journal particularly welcomes research papers that deliver new insights into region-specific hydrological processes and responses to changing conditions, as well as contributions that incorporate interdisciplinarity and translational science.
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