Mapping and modeling groundwater potential using machine learning, deep learning and ensemble learning models in the Saiss basin (Fez-Meknes region, Morocco)
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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引用次数: 0
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.
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.