Comparative Analysis of Drought Modeling and Forecasting Using Soft Computing Techniques

Drought modeling is vital for managing water scarcity in arid regions. It allows proactive planning, resource allocation, and policy development. A combination of statistical models and machine learning techniques is necessary to capture the complexity of drought dynamics effectively. In this study, we compare the performance of the ARIMAX hybrid statistical method and the ANN and Fuzzy-based machine learning method ANFIS for drought modeling. Among the various models examined, the most promising results are obtained using a combination of ANFIS and ARIMAX, which are subsequently employed for drought event forecasting. Notably, ANFIS exhibits lower accuracy for long-term forecasting compared to ARIMAX. The study's novelty lies in the unequivocal demonstration of the ARIMAX (3,0,2) (3,0,2,12) model's superior performance in predicting meteorological drought events. This underscores the potential of ARIMAX models in leveraging historical data for adeptly forecasting drought. Furthermore, this model is applied to multiple locations to generate a drought forecasting and risk map for future years.