Utilising Artificial Intelligence in Groundwater Hydrology for Sustainable Water Resources Management

Although groundwater is an essential resource supporting agricultural practices, ecological systems, and municipal water delivery infrastructures, overuse, pollution, and climate unpredictability are placing it under increasing strain. Because groundwater systems are complex and variable, traditional hydrological models that assess and manage them often encounter challenges. Artificial intelligence (AI) development presents a groundbreaking opportunity to address these challenges effectively. AI holds significant potential for enhancing our understanding, forecasting, and management of groundwater resources due to its ability to analyse large datasets and detect nonlinear interrelationships.

This research proposal uses AI in groundwater hydrology to improve resource management, forecast aquifer behaviour in response to changing climates, and promote sustainable decision-making practices. The supervisor already has massive groundwater data in many areas in the UK.

Research Objectives

• Develop AI models to simulate groundwater frameworks, utilising geological and hydro-meteorological data to predict groundwater levels, recharge rates, and flow dynamics.

• Improve predictive accuracy: Assess the performance of AI models against traditional numerical models (such as MODFLOW) in terms of accuracy, efficiency, and scalability.

• Encourage sustainable decision-making: Provide policymakers with AI-powered decision-support tools to protect aquifers.