Mapping and modeling groundwater potential using machine learning, deep learning and ensemble learning models in the Saiss basin (Fez-Meknes region, Morocco)

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL Groundwater for Sustainable Development Pub Date : 2024-07-09 DOI:10.1016/j.gsd.2024.101281
Hind Ragragui , My Hachem Aouragh , Abdellah El-Hmaidi , Lamya Ouali , Jihane Saouita , Zineb Iallamen , Habiba Ousmana , Hajar Jaddi , Anas El Ouali
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Abstract

The Saïss basin in the Fez-Meknes region of Morocco, covering approximately 2100 km2, faces increased water demand due to population growth, economic development, and climate change, making groundwater a crucial resource. This study aims to delineate areas with groundwater potential (GWP) and evaluate the performance of various machine learning, deep learning, and hybrid ensemble models in predicting GWP. Using a dataset of 440 springs and wells, and 20 groundwater conditioning factors (GWCF) including topographical, hydrological, geological, and hydrogeological features, the study employed multi-collinearity analysis, variance inflation factor (VIF), tolerance (Tol) assessments, and an Information Gain (IG) test to analyze these factors. The study compared the performance of three machine learning algorithms (Gaussian Naive Bayes (GNB), k-Nearest Neighbors (KNN), Gradient Boosting Classifier (GBC)), three deep learning algorithms (Deep Learning Neural Networks (DLNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN)), and a hybrid ensemble model (Random Forests (RF), Support Vector Machine (SVM), Logistic Regression (LR)) using the area under the receiver operating characteristic curve (ROC-AUC) as the evaluation metric. The results showed that the hybrid ensemble model had the highest AUC of 0.86, followed by GBC (AUC = 0.85), DLNN (AUC = 0.84), CNN (AUC = 0.83), KNN (AUC = 0.79), RNN (AUC = 0.78), and GNB (AUC = 0.75). The study revealed that 45% of the Saïss Basin exhibits high to very high GWP, particularly in Ain Taoujdat, Haj Kaddour, and Boufekrane districts, with lithology, slope, and transmissivity being the most influential factors. The resulting GWP map can guide decision-makers in planning well and borehole drilling for drinking water and agriculture, as well as artificial recharge projects, thus promoting sustainable groundwater management in the Saïss basin.

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在赛斯盆地(摩洛哥非斯-梅克内斯地区)利用机器学习、深度学习和集合学习模型绘制地下水潜力图并建立地下水潜力模型
摩洛哥非斯-梅克内斯地区的塞斯盆地面积约 2100 平方公里,由于人口增长、经济发展和气候变化,该地区面临着日益增长的用水需求,地下水因此成为一种重要资源。本研究旨在划定具有地下水潜力(GWP)的区域,并评估各种机器学习、深度学习和混合集合模型在预测 GWP 方面的性能。该研究使用了一个包含 440 口泉水和水井以及 20 个地下水条件因子(GWCF)(包括地形、水文、地质和水文地质特征)的数据集,并采用了多重共线性分析、方差膨胀因子(VIF)、容忍度(Tol)评估和信息增益(IG)测试来分析这些因子。研究比较了三种机器学习算法(高斯直观贝叶斯算法(GNB)、k-最近邻算法(KNN)、梯度提升分类器(GBC))、三种深度学习算法(深度学习神经网络(DLNN)、卷积神经网络(CNN)、循环神经网络(RCN))的性能、以及一种混合集合模型(随机森林(RF)、支持向量机(SVM)、逻辑回归(LR)),使用接收器工作特征曲线下面积(ROC-AUC)作为评估指标。结果显示,混合集合模型的 AUC 最高,为 0.86,其次是 GBC(AUC = 0.85)、DLNN(AUC = 0.84)、CNN(AUC = 0.83)、KNN(AUC = 0.79)、RNN(AUC = 0.78)和 GNB(AUC = 0.75)。研究显示,萨伊斯盆地 45% 的地区显示出较高或极高的 GWP,尤其是在 Ain Taoujdat、Haj Kaddour 和 Boufekrane 地区,其中岩性、坡度和渗透率是影响最大的因素。由此绘制的全球升水潜能值地图可指导决策者规划饮用水和农业用水的打井和钻孔,以及人工补给项目,从而促进萨伊斯盆地的可持续地下水管理。
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来源期刊
Groundwater for Sustainable Development
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.
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