Amid the escalating impacts of climate change, droughts are becoming increasingly frequent and severe, necessitating advanced monitoring and predictive strategies to mitigate their adverse effects on agriculture, water resources, and ecosystems. This research leverages state-of-the-art machine learning techniques and an extensive multi-source dataset—including satellite imagery, meteorological data, soil characteristics, and historical drought records—to develop an AI-driven framework for drought monitoring and early prediction. The study employs Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to capture complex spatial and temporal patterns, enabling more accurate and timely drought forecasting compared to traditional approaches. The proposed system offers real-time alerts and forecasts, facilitating proactive water resource management, agricultural planning, and disaster preparedness. Moreover, the research conducts a comparative evaluation of various machine learning models to identify the most effective algorithms for different climatic zones and crop types. By enhancing the precision and reliability of drought prediction systems, this study contributes to the promotion of sustainable agriculture and climate resilience. The research culminates in a scalable, adaptable, and institutionalized AI framework that addresses urgent environmental challenges. The outcomes aim to empower policymakers, farmers, and researchers with data-driven insights for informed decision-making, fostering a resilient agricultural ecosystem. This work underscores the transformative potential of AI in environmental monitoring and supports the broader goal of sustainable environmental development.
Vij et al. (Fri,) studied this question.