Integrating Artificial Intelligence, Numerical Methods and Remote Sensing for Advanced Groundwater Modelling: A Comprehensive Review
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Abstract
Groundwater depletion, climate change, and rising water demand are the primary challenges to the sustainable management of water resources in arid/semi-arid areas, such as Iraq. This study evaluates current models for groundwater and their performance and assesses the recent advancements in groundwater modeling techniques. It addresses that currently, numerical models, Artificial intelligence (AI), and remote sensing techniques are being implemented separately. Physically based models are widely used in groundwater modelling due to their reliable performance in simulating groundwater flow and transport processes. It was widely used in Iraqi and international groundwater studies, constituting 62% and 60% of groundwater studies in those regions. In contrast, the application of AI-based tools offers great potential in terms of predicting groundwater dynamics' nonlinear behavior, improved predictive results, and a data-driven assessment of hydrogeological conditions. The use of remote sensing-based techniques such as GRACE and GLDAS has become more popular among researchers as a means of estimating changes in groundwater storage volumes. However, its application in studies is rather limited and amounts to around 28% of studies conducted in Iraq and 23% of international research. It is evident from the review that the use of hybrid approaches can greatly improve predictions and groundwater management.
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