Application of fuzzy rule-based modeling technique to regional drought

Fuzzy rule-based modeling is applied to the prediction of regional droughts (characterized by the modified Palmer index, PMDI) using two forcing input s, El Nino/Southern Oscillation (ENSO) and large scale atmospheric circulat ion patterns (CPs) in a typical Great Plains state, Nebraska. Although, the re is significant relationship between simultaneous monthly CP, lagged Sout hern Oscillation Index (SOI) and PMDI in Nebraska, the weakness of the corr elations, the dependence between CP and SOI and the relatively short data s et limit the applicability of statistical modeling for prediction. Due to t he above difficulties, a fuzzy rule-based approach is presented to predict PMDI from monthly frequencies of daily CP types and lagged prior SOIs. The fuzzy rules are defined and calibrated using a subset called the learning s et of the observed time series of premises and PMDI response. Then, another subset, the validation set is used to check how the application of fuzzy r ules reproduces the observed PMDI. In all its eight climate divisions and N ebraska itself the fuzzy rule-based technique using the joint forcing of CP and SOI, is able to learn the high variability and persistence of PMDI and results in almost perfect reproduction of the empirical frequency distribu tions. (C) 1999 Elsevier Science B.V. All rights reserved.