利用人工智能预测对干旱和半干旱地区灌溉水质的争议性见解：加贝斯南部案例

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development Pub Date : 2024-11-01 DOI:10.1016/j.gsd.2024.101381

Khyria Wederni , Boulbaba Haddaji , Younes Hamed , Salem Bouri , Nicolò Colombani

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引用次数: 0

摘要

在干旱和半干旱地区，有效的地下水管理至关重要，因为水资源对农业至关重要。本研究采用传统水化学分析和机器学习模型（特别是分类回归树（CART）和支持向量机（SVM））相结合的方法，对突尼斯南部加贝斯含水层的灌溉水质量指数（IWQI）进行了评估。共对 83 个地下水样本进行了基于五个关键参数的分析：电导率 (EC)、钠吸附率 (SAR)、氯化物 (Cl-)、钠 (Na+) 和碳酸氢盐 (HCO3-)。结果表明，CART 模型性能优越，R2 值为 0.99，均方根误差 (RMSE) 为 0.43，而 SVM 模型的 R2 值为 0.87。对地下水质量进行了分类，结果显示 62% 的样本被归类为 "令人满意"，可用于灌溉，而 31% 的样本未经处理即被视为 "不适合"，这凸显了农业用水的关注领域。这项研究还强调了持续监测和适应性管理策略对确保该地区可持续用水的重要性。总体而言，这项研究证明了机器学习模型，尤其是 CART，在准确评估地下水质量方面的有效性。这些见解为资源管理人员做出明智决策提供了宝贵的工具，确保了干旱和半干旱地区地下水的可持续开发。这些发现为今后水资源管理方面的研究和政策制定铺平了道路。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Controversial insights into irrigation water quality in arid and semi-arid regions using AI driven predictions: Case of southern Gabès

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Controversial insights into irrigation water quality in arid and semi-arid regions using AI driven predictions: Case of southern Gabès

Effective groundwater management is critical in arid and semi-arid regions, where water resources are essential for agriculture. This study assesses the Irrigation Water Quality Index (IWQI) of the Southern Gabès aquifer in Tunisia using a combination of traditional hydrochemical analysis and machine learning models—specifically, Classification and Regression Tree (CART) and Support Vector Machine (SVM). A total of 83 groundwater samples were analyzed based on five key parameters: Electrical Conductivity (EC), Sodium Adsorption Ratio (SAR), Chloride (Cl-), Sodium (Na+), and Bicarbonate (HCO3-). The results show that the CART model demonstrated superior performance with an R² value of 0.99 and a Root Mean Square Error (RMSE) of 0.43, while the SVM model achieved an R² of 0.87. These findings underscore CART's robustness in predicting IWQI, offering high precision even with limited datasets.

The groundwater quality was categorized, revealing that 62% of samples were classified as "satisfactory" for irrigation, while 31% were deemed "unsuitable" without treatment, highlighting areas of concern for agricultural use. The study also emphasizes the importance of continuous monitoring and adaptive management strategies to ensure sustainable water use in the region.

Overall, this research demonstrates the effectiveness of machine learning models, particularly CART, in accurately assessing groundwater quality. These insights provide valuable tools for resource managers to make informed decisions, ensuring the sustainable exploitation of groundwater in arid and semi-arid regions. The findings pave the way for future research and policy development in water resource management.

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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.