Advancing AI Models for Drought Forecasting, Challenges and Future Directions

Drought, a persistent natural disaster, poses significant challenges to ecosystems, agriculture, water resources and socio-economic stability worldwide. The evolution of drought forecasting models reflects a continuous pursuit of greater accuracy and lead time, particularly highlighted by events like the 2012 US Midwest drought. Traditional models relying on empirical relationships have faced limitations in capturing the complexities of meteorological droughts, leading to the exploration of AI-driven approaches. This comprehensive review explores the landscape of drought forecasting, focusing on the integration of AI and hybrid models. The advantages of AI, such as its ability to handle nonlinear relationships and vast datasets, have revolutionized drought assessment and prediction. Various AI techniques including Neural Networks, Support Vector Machines, Fuzzy Logic and Deep Learning, offer unprecedented accuracy and real-time monitoring capabilities. Hybrid models, combining AI with traditional statistical or dynamical approaches, show promise in enhancing predictive capabilities. The integration of Wavelet Transform with Neural Networks and other hybrid strategies has demonstrated success in capturing non-linear relationships and improving prediction accuracy. The future of AI-driven drought forecasting lies in collaborative efforts, innovative research and ethical practices. By navigating these challenges and seizing opportunities, AI models can contribute significantly to building resilience and sustainable management of water resources globally.