Saeid MehdizadehFarshad AhmadiAli Danandeh MehrMir Jafar Sadegh SafariMehr, Ali DanandehSafari, Mir Jafar SadeghAhmadi, FarshadDanandeh Mehr, AliMehdizadeh, Saeid2025-10-0620200022-16941879-270710.1016/j.jhydrol.2020.1250172-s2.0-85089413651http://dx.doi.org/10.1016/j.jhydrol.2020.125017https://gcris.yasar.edu.tr/handle/123456789/7598https://doi.org/10.1016/j.jhydrol.2020.125017The standardized precipitation evapotranspiration index (SPEI) at three different time scales (i.e. SPEI-3 SPEI-6 and SPEI-12) from six meteorology stations located in Turkey are modeled in this study. To this end two types of classic time series models namely linear autoregressive (AR) and non-linear bi-linear (BL) are used. Furthermore the hybrid models are proposed by coupling the wavelet (W) and gene expression programming (GEP). Five various mother wavelets (i.e. Haar db4 Symlet Meyer and Coifflet) for the first time are employed and compared for implementing the hybrid W-GEP approach in drought modeling. The modeling results of SPEI droughts via the time series models illustrated that the non-linear BL performs slightly better than the linear AR. Moreover all the hybrid W-GEP models developed in the study region provide superior performances compared to the standalone GEP. In general db4 in SPEI-3 modeling and Symlet for modeling the SPEI-6 and SPEI-12 of the studied locations are the optimal wavelets to develop the W-GEP. Finally the SPEI series at each target station is modeled through applying the SPEI data of the five neighboring stations. It is found that the SPEI data of neighboring stations are appropriate for modeling the SPEI series of the target station when the SPEI data of the target station is not at hand. For this case the performance of standalone GEP for modeling the SPEI-3 and SPEI-6 of the stations is generally better than the case of utilizing the original SPEI data at each target station.Englishinfo:eu-repo/semantics/closedAccessDrought modeling, Time series models, Hybrid models, Wavelet analysis, Gene expression programmingSUPPORT VECTOR REGRESSION, ARTIFICIAL-INTELLIGENCE, RIVER-BASIN, STANDARDIZED PRECIPITATION, CLIMATE INDEXES, NEURAL-NETWORK, MACHINE, PREDICTION, VARIABILITY, PERFORMANCEGene Expression ProgrammingHybrid ModelsDrought ModelingWavelet AnalysisTime Series ModelsArticle