Abstract
Investigators publish new report on artificial intelligence. According to news reporting from Mohammed Ⅵ Polytechnic University by NewsRx journalists, research stated, “The Plio-Quaternary aquifer in the EL-Haouz-Mejjate region of Morocco is critical for water supply, necessitating accurate characterization for sustainable management. This study pioneers machine learning (ML) and deep learning (DL) techniques to elucidate the aquifer’s properties.” Our news correspondents obtained a quote from the research from Mohammed Ⅵ Polytechnic University: “Supervised algorithms, including random forest, regression, support vector machines, Gaussian process regression and neural networks, are trained on available hydrogeological data. Diverse features capture complex input-output relationships to predict key hydrodynamic factors like hydraulic conductivity and transmissivity fields. Aquifer architecture attributes, including substratum depth, thickness, and height, are also estimated. Model outputs are validated with field measurements, demonstrating promising accuracy. Enhanced hydrodynamic insights improve the conceptual model and groundwater flow modeling confidence. Uncertainties are reduced through this data-driven approach, enabling optimized aquifer management.”
NETL
NSTL