figures of paper : A physics-informed LSTM deep learning framework for simulating groundwater decline and salinisation in an overexploited semi-arid aquifer (NSSA basin, central Tunisia)
Domain:
environment and energyRecord type:
paper
Creator:
bah
Publisher:
Host:
Similar
Deep Learning Approach with LSTM for Daily Streamflow Prediction in a Semi-Arid Area: A Case Study of Oum Er-Rbia River Basin, MoroccoIrrigation-Driven Groundwater Recharge and Quality Degradation in Semi-Arid Regions: Hydrochemical, GIS-Based, and Explainable Machine Learning Assessment in Central TunisiaA Hybrid Deep Learning and Remote Sensing Framework for Climate-Smart Livestock Grazing in Semi-Arid NigeriaAn isotopic study of surface water and groundwater in a semi-arid environment: Souss-Massa basin in SW MoroccoDevelopment of a Distributed Physics‐Informed Deep Learning Hydrological Model for Data‐Scarce RegionsDisentangling Natural Mineralization from Nitrate Pollution in a Semi-Arid Alluvial Aquifer: A Log-Transformation-Based Multivariate and GIS Framework (Tébessa-Morsott Basin, Algeria)