Groundwater Level Modeling Artificial Intelligence Approach of Features and Abilities

Creating precise soft computing techniques for estimating groundwater levels (GWL) to improve water resource scheduling and administration is crucial. Machine Learning (ML) algorithms have significantly improved GWL projection over the last twenty years. This research briefly summarizes the most widely used Artificial intelligence (AI) techniques and reviews the limited edition on AI approaches for groundwater level (GWL) modeling and estimation. Despite some flaws, the GWL temporal sequence in various groundwater can be effectively simulated and forecasted using AI methods if it is designed correctly. Examining these stages in the context of GWL modeling is advantageous due to the reliance of some phases of AI modeling on knowledge acquisition or iterative experimentation. The articles under review produced many limited and broad outcomes that can serve as valuable guidelines for scientists who wish to conduct comparative research in this area. This study also offers a few fresh concepts in the related field of inquiry for creating novel techniques and enhancing modeling quality.