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Methodsx|March 3, 2023
An approach based on multivariate distribution and Gaussian copulas to predict groundwater quality using DNN models in a data scarce environmentAyoub Nafii, Houda Lamane, Abdeslam Taleb, et al.Environmental Science and Pollution Research International|July 10, 2024
Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretabilityAli El Bilali, Youssef Brouziyne, Oumaima Attar, et al.Journal of Environmental Management|December 2, 2022
An interpretable machine learning approach based on DNN, SVR, Extra Tree, and XGBoost models for predicting daily pan evaporationAli El Bilali, Taleb Abdeslam, Nafii Ayoub, et al.Scientific Reports|October 21, 2025
An interpretable machine learning approach based on SHAP, Sobol and LIME values for precise estimation of daily soybean crop coefficientsAhmed Elbeltagi, Aman Srivastava, Xinchun Cao, et al.Environmental Science and Pollution Research International|February 19, 2022
Predicting daily pore water pressure in embankment dam: Empowering Machine Learning-based modelingAli El Bilali, Mohammed Moukhliss, Abdeslam Taleb, et al.Pageof 1