Groundwater hydro-geochemical inferences and eXplainable Artificial Intelligence augmented groundwater quality prediction in arid and semi-arid segment of Rajasthan, India

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development Pub Date : 2024-07-04 DOI:10.1016/j.gsd.2024.101272

Sunita , Tathagata Ghosh

{"title":"Groundwater hydro-geochemical inferences and eXplainable Artificial Intelligence augmented groundwater quality prediction in arid and semi-arid segment of Rajasthan, India","authors":"Sunita , Tathagata Ghosh","doi":"10.1016/j.gsd.2024.101272","DOIUrl":null,"url":null,"abstract":"<div><p>Groundwater quality is a crucial aspect especially in the arid and semi-arid segments of the world due to its restricted availability. With increasing consumptions over time period, it is essential to ensure its quality by appraising complex hydro-geochemistry. In the present study, an attempt has been made to evaluate the groundwater hydro-geochemistry in the arid and semi-arid segment of Rajasthan, India and to fill the gap in understanding of groundwater quality by incorporating eXplainable Artificial Intelligence (XAI). 120 groundwater samples were collected during post monsoon season of 2022 and sixteen physico-chemical parameters were analyzed and corresponding inferences were drawn. The hydro-chemical facies indicated Na–Cl composition of groundwater with the dominance of evaporation. Majority of the samples showed reverse ion exchange process along with positive Saturation Index value of Calcite (CaCO<sub>3</sub>) and tendency towards leaching F<sup>−</sup> in the groundwater. Water Quality Index for drinking as well as irrigation purpose showed relatively better quality in the central segment than the marginal region. The SHAP values derived from the XGBoost model depicted fluoride (F-) as the primary feature influencing overall groundwater quality for drinking purposes, whereas the Sodium Absorption Ratio (SAR) emerged as the key predictor influencing overall groundwater quality for irrigation. The implication of proposed method signifies the importance of incorporating hydro-geochemical inferences with machine learning technique to understand the complex character of groundwater. Further, due to its robustness as well as cost-effectiveness, the application of the method would be helpful in policymaking to safeguard the groundwater resource in arid and semi-arid regions at global scale.</p></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"26 ","pages":"Article 101272"},"PeriodicalIF":4.9000,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Groundwater for Sustainable Development","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352801X24001954","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}

引用次数: 0

Abstract

Groundwater quality is a crucial aspect especially in the arid and semi-arid segments of the world due to its restricted availability. With increasing consumptions over time period, it is essential to ensure its quality by appraising complex hydro-geochemistry. In the present study, an attempt has been made to evaluate the groundwater hydro-geochemistry in the arid and semi-arid segment of Rajasthan, India and to fill the gap in understanding of groundwater quality by incorporating eXplainable Artificial Intelligence (XAI). 120 groundwater samples were collected during post monsoon season of 2022 and sixteen physico-chemical parameters were analyzed and corresponding inferences were drawn. The hydro-chemical facies indicated Na–Cl composition of groundwater with the dominance of evaporation. Majority of the samples showed reverse ion exchange process along with positive Saturation Index value of Calcite (CaCO₃) and tendency towards leaching F⁻ in the groundwater. Water Quality Index for drinking as well as irrigation purpose showed relatively better quality in the central segment than the marginal region. The SHAP values derived from the XGBoost model depicted fluoride (F-) as the primary feature influencing overall groundwater quality for drinking purposes, whereas the Sodium Absorption Ratio (SAR) emerged as the key predictor influencing overall groundwater quality for irrigation. The implication of proposed method signifies the importance of incorporating hydro-geochemical inferences with machine learning technique to understand the complex character of groundwater. Further, due to its robustness as well as cost-effectiveness, the application of the method would be helpful in policymaking to safeguard the groundwater resource in arid and semi-arid regions at global scale.

Abstract Image

查看原文本刊更多论文

印度拉贾斯坦邦干旱和半干旱地区的地下水水文地球化学推断和可扩展人工智能地下水质量预测

由于地下水供应有限，地下水质量是一个至关重要的方面，尤其是在世界上的干旱和半干旱地区。随着消耗量的不断增加，必须通过评估复杂的水文地球化学来确保地下水的质量。本研究试图对印度拉贾斯坦邦干旱和半干旱地区的地下水水文地球化学进行评估，并通过结合可扩展人工智能（XAI）填补对地下水质量了解的空白。在 2022 年季风后季节收集了 120 个地下水样本，分析了 16 个物理化学参数，并得出了相应的推论。水化学面表明，地下水的成分为 Na-Cl，以蒸发为主。大部分样本显示了反向离子交换过程，方解石（CaCO3）饱和度指数值为正，地下水有沥滤 F- 的趋势。用于饮用和灌溉的水质指数显示，中部地区的水质相对好于边缘地区。根据 XGBoost 模型得出的 SHAP 值显示，氟化物（F-）是影响饮用地下水总体水质的主要特征，而钠吸收比（SAR）则是影响灌溉地下水总体水质的关键预测因子。所提出方法的意义表明，将水文地球化学推断与机器学习技术相结合，对于了解地下水的复杂特性非常重要。此外，由于其稳健性和成本效益，该方法的应用将有助于在全球范围内制定政策，保护干旱和半干旱地区的地下水资源。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Groundwater for Sustainable Development Social Sciences-Geography, Planning and Development

CiteScore

11.50

自引率

10.20%

发文量

152

期刊介绍： Groundwater for Sustainable Development is directed to different stakeholders and professionals, including government and non-governmental organizations, international funding agencies, universities, public water institutions, public health and other public/private sector professionals, and other relevant institutions. It is aimed at professionals, academics and students in the fields of disciplines such as: groundwater and its connection to surface hydrology and environment, soil sciences, engineering, ecology, microbiology, atmospheric sciences, analytical chemistry, hydro-engineering, water technology, environmental ethics, economics, public health, policy, as well as social sciences, legal disciplines, or any other area connected with water issues. The objectives of this journal are to facilitate: • The improvement of effective and sustainable management of water resources across the globe. • The improvement of human access to groundwater resources in adequate quantity and good quality. • The meeting of the increasing demand for drinking and irrigation water needed for food security to contribute to a social and economically sound human development. • The creation of a global inter- and multidisciplinary platform and forum to improve our understanding of groundwater resources and to advocate their effective and sustainable management and protection against contamination. • Interdisciplinary information exchange and to stimulate scientific research in the fields of groundwater related sciences and social and health sciences required to achieve the United Nations Millennium Development Goals for sustainable development.