Coupling fuzzy-SVR and boosting-SVR models with wavelet decomposition for meteorological drought prediction

Drought is a climatic occurrence of prolonged and abnormal moisture deficiency resulting from meteorological anomalies. Despite its negative impact to agricultural activity and water resources management, drought is still a poorly comprehended calamity, primarily due to the difficulties ascertaning its onset. Effective drought prediction is important for any development of a sustainable natural environment. This study discusses the wavelet-boosting-support vector regression (W-BS-SVR), multi-input wavelet-fuzzy-support vector regression (multi-input W-F-SVR) and weighted wavelet-fuzzy-support vector regression (weighted W-F-SVR) models for meteorological drought predictions, at the downstream of the Langat River Basin; with lead times of 1 month, 3 months, and 6 months. Drought severity is described by the Standardized Precipitation Evapotranspiration Indices (SPEIs) with different timescales of 1 month, 3 months, and 6 months, respectively, known as SPEI-1, SPEI-3, and SPEI-6. The observed SPEIs from 1976 to 2007 were used for model training, while the SPEIs from 2008 to 2015 were for model validation. The root-mean-square-error (RMSE), mean absolute error (MAE), coefficient of determination (R-2), and adjusted R-2 were applied to assess the performance of models. In general, it was found that the fuzzy-based hybrid model, the weighted W-F-SVR predicted well for SPEI-1, SPEI-3, and SPEI-6 cases, with lead times of 3 and 6 months. As for the 1-month lead time predictions, the models' performances were dominated by the temporal variation in the SPEIs, where the weighted W-F-SVR that is capable in reducing outlier effects, performed best for high variation SPEI-1 and SPEI-3, while the W-BS-SVR model was better for SPEI-6.