Machine Learning Approaches for Spatial Modeling of Agricultural Droughts

Dear Colleagues, 

Drought is often considered as one of the most serious natural hazards causing a significant impact on a wide range of social, economic, and ecological systems. Climate change increases droughts recurrence, severity, duration, and spatial extent. Understanding and modelling drought and its impacts is of paramount importance for society and may serve as a critical mitigation practice for sustainable development. The aim of this Special Issue is to explore innovative technologies to forecast and monitor droughts at unprecedented levels of accuracy and resolution that can help to improve the profitability and productivity of agriculture during times of droughts.

In line with the context and aims outlined above, we invite original contributions on (but not limited to) the following topics: 

• Spatial modelling of droughts;
• Machine learning approaches for prediction of agricultural (spatial) droughts;
• Big data analytics for drought modelling and prediction;
• Drought risk analysis using state-of-the-art techniques, variability and adaptation.

Dr. Kavina Dayal
Dr. Thong Nguyen-Huy
Guest Editors

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