caa a22 4500 445800135 CHVBK 20180317145138.0 cr unu---uuuuu 170323e20110501xx s 000 0 eng 10.1007/s00704-010-0317-4 doi (NATIONALLICENCE)springer-10.1007/s00704-010-0317-4 Application of global SST and SLP data for drought forecasting on Tehran plain using data mining and ANFIS techniques [Elektronische Daten] [Ashkan Farokhnia, Saeed Morid, Hi-Ryong Byun] 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 9months 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. Springer-Verlag, 2010 Farokhnia Ashkan College of Agriculture, Tarbiat Modares University, Tehran, Iran aut Morid Saeed College of Agriculture, Tarbiat Modares University, Tehran, Iran aut Byun Hi-Ryong Department of Environmental Atmospheric Sciences, Pukyong National University, Busan, Republic of Korea aut Theoretical and Applied Climatology Springer Vienna 104/1-2(2011-05-01), 71-81 0177-798X 104:1-2<71 2011 104 704 https://doi.org/10.1007/s00704-010-0317-4 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00704-010-0317-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Farokhnia Ashkan College of Agriculture, Tarbiat Modares University, Tehran, Iran aut NATIONALLICENCE 700 1- Morid Saeed College of Agriculture, Tarbiat Modares University, Tehran, Iran aut NATIONALLICENCE 700 1- Byun Hi-Ryong Department of Environmental Atmospheric Sciences, Pukyong National University, Busan, Republic of Korea aut NATIONALLICENCE 773 0- Theoretical and Applied Climatology Springer Vienna 104/1-2(2011-05-01), 71-81 0177-798X 104:1-2<71 2011 104 704 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer