Stochastic Environmental Research and Risk Assessment

, Volume 23, Issue 8, pp 1143–1154

Adaptive Neuro-Fuzzy Inference System for drought forecasting

Original Paper

DOI: 10.1007/s00477-008-0288-5

Cite this article as:
Bacanli, U.G., Firat, M. & Dikbas, F. Stoch Environ Res Risk Assess (2009) 23: 1143. doi:10.1007/s00477-008-0288-5

Abstract

Drought causes huge losses in agriculture and has many negative influences on natural ecosystems. In this study, the applicability of Adaptive Neuro-Fuzzy Inference System (ANFIS) for drought forecasting and quantitative value of drought indices, the Standardized Precipitation Index (SPI), is investigated. For this aim, 10 rainfall gauging stations located in Central Anatolia, Turkey are selected as study area. Monthly mean rainfall and SPI values are used for constructing the ANFIS forecasting models. For all stations, data sets include a total of 516 data records measured between in 1964 and 2006 years and data sets are divided into two subsets, training and testing. Different ANFIS forecasting models for SPI at time scales 1–12 months were trained and tested. The results of ANFIS forecasting models and observed values are compared and performances of models were evaluated. Moreover, the best fit models have been also trained and tested by Feed Forward Neural Networks (FFNN). The results demonstrate that ANFIS can be successfully applied and provide high accuracy and reliability for drought forecasting.

Keywords

Drought forecastingANFISDrought indicesCentral AnatoliaTurkey

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Ulker Guner Bacanli
    • 1
  • Mahmut Firat
    • 1
  • Fatih Dikbas
    • 1
  1. 1.Civil Engineering Department, Faculty of EngineeringPamukkale UniversityDenizliTurkey