Improving Drought Prediction Accuracy: A Hybrid EEMD and Support Vector Machine Approach with Standardized Precipitation Index

This work combines the Support Vector Machine (SVM) model with Ensemble Empirical Mode Decomposition (EEMD) to present a novel method for drought prediction. The EEMD-SVM model is assessed for drought forecasting, comparing it to conventional Auto Regressive Integrated Moving Average (ARIMA) and SVM models. This study uses monthly precipitation data from Bamyan province in Central Afghanistan, spanning January 1970 to December 2019, including Standardized Precipitation Index (SPI) timelines: SPI 3, SPI 6, SPI 9, and SPI 12. To evaluate predictive effectiveness, statistical measures such as R-squared (R²), mean absolute error (MAE), and root-mean-square error (RMSE) are used. Each SPI series is decomposed into Intrinsic Mode Functions (IMFs) and a residual series using the EEMD approach. The next stage projects each IMF component and residual using the appropriate SVM model. The final step creates an ensemble forecast for the original SPI series by combining the anticipated results of the residual series with the modeled IMFs. Compared to traditional ARIMA and SVM models, results show that the EEMD-SVM technique greatly improves drought forecasting accuracy, especially for mid- and long-term SPI. For example, in the testing period, SPI 9 yields an RMSE of 0.1632, MAE of 0.1208, and R² of 0.9357, while for SPI 12, RMSE is 0.1078, MAE is 0.0745, and R² is 0.9141, indicating the best criteria with the lowest RMSE and MAE and highest R² compared to conventional ARIMA and SVM. This novel technology could enhance the capacity to forecast drought episodes, leading to more efficient water resource management and climate adaptation plans.