Hybrid wavelet-artificial intelligence models in meteorological drought estimation

TAYLAN E. D., Terzi O., Baykal T.

JOURNAL OF EARTH SYSTEM SCIENCE, vol.130, no.1, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 130 Issue: 1
  • Publication Date: 2021
  • Doi Number: 10.1007/s12040-020-01488-9
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Environment Index, Geobase
  • Süleyman Demirel University Affiliated: Yes


In this study, wavelet transform (W), which is one of the data pre-processing techniques, adaptive neural-based fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural networks (ANNs) were used to develop the drought estimation models of Canakkale, Turkey. For these models, 3-, 6-, 9- and 12-months drought indices were calculated by standard precipitation index (SPI) and by using precipitation data of Canakkale, Gokceada and Bozcaada stations between 1975 and 2010 years. Firstly, ANFIS, SVM and ANNs models were developed to estimate calculated drought indices. Then SPI values of Gokceada and Bozcaada stations were divided into sub-series by wavelet transform technique and these sub-series were used as input in W-ANFIS, W-SVM and W-ANNs models. When the developed models were compared, it was determined that the hybrid models developed by using preprocessing technique performed better. Among these models, it was observed that the W-ANFIS model gave the best results for 6-months period.Research HighlightsCalculating of 3-, 6-, 9- and 12- months meteorological drought index with SPIDeveloping ANFIS, SVM and ANNs drought models using SPI valuesDecomposition of SPI values into sub-series by wavelet transform technique and developing hybrid drought models (W-ANFIS, W-SVM and W-ANNs) using subseries of SPI valuesComparing ANFIS, SVM and ANNs models with hybrid modelsObtaining appropriate results with hybrid models in meteorological drought estimation