Advancing AI Models
for Drought Forecasting, Challenges and Future Directions
Shaikh Ayub, Patil Uday, Shelar Vaibhav and Phatak Udaykumar
Disaster Advances; Vol. 17(12); 58-71;
doi: https://doi.org/10.25303/1712da058071; (2024)
Abstract
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.
