Drought prediction over the East Asian monsoon region using the adaptive neuro‐fuzzy inference system and the global sea surface temperature anomalies

Prediction of droughts has a great importance in the management and planning of water resources. This study developed an adaptive neuro‐fuzzy inference system ( ANFIS ) based model for prediction of droughts, and evaluated its applicability in the seven homogeneous rainfall zones of the East Asian monsoon region (20°–50°N, 103°–149°E). Standardized Precipitation Index ( SPI ) was used to characterize the drought events. SPI series were computed for each zone using a 30‐year (1978–2007) gridded rainfall dataset (0.5° grid resolution) at the corresponding grid points. The influence of sea surface temperature anomalies ( SSTA ) on droughts was assessed using a lagged‐correlation between global gridded SSTA (0.2° grid resolution) and the SPI of each zone. SSTA were used as a potential predictor variable based on the premise that the land‐sea thermal contrast is a major driver of the monsoon. The model was trained and validated using a 25‐year (1978–2002) dataset, with different configurations to obtain the optimum model structure and a set of suitable predictors. The performance of the model was demonstrated by comparing the model simulated results with the observed drought index and drought categories using a 5‐year (2003–2007) independent checking dataset. The model predicted the drought categories accurately for 50 to 70% cases in checking period for different zones. The results showed the viability of the proposed model for drought prediction with substantial enhancement in accuracy when past SSTA were used as a predictor compared with the use of only past SPI data.