AI-Driven Water Insecurity Prediction Using Satellite Data: A Framework for the Sahel Region

Water insecurity poses a critical threat to livelihoods and environmental stability in the Sahelregion of Africa, where climate variability, erratic rainfall, and limited infrastructure underminesustainable water access. This project proposes the development of an AI-driven framework for predicting water insecurity using satellite-derived data on precipitation, vegetation health, soil moisture, and surface water dynamics. By integrating deep learning models, specifically Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, this research aims to capture both spatial and temporal patterns of water stress across the Sahel. The study will generate predictive risk maps to identify vulnerable areas, offering an essential tool for policymakers, NGOs, and local governments to enhance early warning systems, guide resource allocation, and strengthen regional resilience against climate-induced water crises. Through its scalable, data-driven approach, the project addresses urgent challenges at the intersection of climate change, water security, and sustainable development in the Global South, with potential applications in similarly vulnerable regions worldwide.