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Adaptive Neuro-Fuzzy Inference System for drought forecasting

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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.

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Acknowledgments

The authors are grateful for editors and anonymous reviewers for their helpful and constructive comments on an earlier draft of this paper.

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Authors and Affiliations

  1. Civil Engineering Department, Faculty of Engineering, Pamukkale University, 20017, Denizli, Turkey

    Ulker Guner Bacanli, Mahmut Firat & Fatih Dikbas

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  1. Ulker Guner Bacanli
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  2. Mahmut Firat
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  3. Fatih Dikbas
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Correspondence to Mahmut Firat.

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Bacanli, U.G., Firat, M. & Dikbas, F. Adaptive Neuro-Fuzzy Inference System for drought forecasting. Stoch Environ Res Risk Assess 23, 1143–1154 (2009). https://doi.org/10.1007/s00477-008-0288-5

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