Application of global SST and SLP data for drought forecasting on Tehran plain using data mining and ANFIS techniques

Drought forecasting is a critical component of drought risk management. Identification of effective predictors is a major component of forecasting models. Sea surface temperature (SST) and sea level pressure (SLP) are relevant predictors for short- to long-term drought forecasts. However, these datasets are captured globally within a cell-wise network. This paper describes an approach to locate the most effective cells of the SST and SLP datasets using data mining. They are then applied as input to an adaptive neurofuzzy inference system (ANFIS) model to forecast possible droughts 3, 6, and 9 months in advance. Tehran plain was selected as the study area, and drought events are designated using the effective drought index (EDI). In another treatment, past values of the EDI time series were introduced to the ANFIS and the results compared with the previous findings. It was shown that R (2) values were higher for all cases applying the SST/SLP datasets. Additionally, the performance of SST/SLP datasets and the ANFIS model was assessed according to drought or wet classification, and it was concluded that more than 90% of the time the ANFIS model detected the drought status correctly or with only a one class error.