Modelling and Simulation of Univariate Model of ‘Groundwater’ and Multivariate Model of ‘Rainfall, Temperature, Root and Topsoil, Depth to Groundwater Table’ Using Deep Learning and Machine Learning Analysis for Time Series Forecasting of Neural Netw

This study investigates modeling and simulation of groundwater dynamics using univariate and multivariate time series forecasting. While the univariate analysis focuses on the water table, the multivariate analysis integrates relevant variables such as precipitation, temperature, root and surface soil cover, and depth to the water table. The incorporation of advanced computational systems such as deep learning and machine learning significantly improves the analytical accuracy and model robustness compared to traditional numerical approaches. The main outcomes of this study include an extension of the projection model that can estimate groundwater levels based on existing historical data of relevant variable quantities. The developed model can help policymakers and stakeholders make informed decisions regarding groundwater utilization and conservation.