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 (R2), and adjusted R2 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.