AI-Enabled Drought Prediction System for Zambia

Abstract

Drought poses significant challenges to agriculture, water resources, and socio-economic stability, particularly in Zambia. This paper presents an AI-driven drought prediction system that integrates historical climate data, IoT-based real-time inputs, and machine learning algorithms, including ensemble models such as Random Forest, XGBoost, and LSTMs. The system provides accurate and localized forecasts, enabling proactive decision-making by farmers, policymakers, and disaster management agencies. Unlike traditional systems, it emphasizes regional adaptability and dynamic model retraining to ensure reliability under evolving climate patterns. Results demonstrate prediction accuracies above 90%, with ensemble approaches outperforming single models. This research highlights the potential of AI in mitigating the impact of climate change by enhancing resilience in drought-prone regions.