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Drought prediction using co-active neuro-fuzzy inference system, validation, and uncertainty analysis (case study: Birjand, Iran)

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Abstract

This work aims to assess the capability of co-active neuro-fuzzy inference system (CANFIS) for drought forecasting of Birjand, Iran through the combination of global climatic signals with rainfall and lagged values of Standardized Precipitation Index (SPI) index. Using stepwise regression and correlation analyses, the signals NINO 1 + 2, NINO 3, Multivariate Enso Index, Tropical Southern Atlantic index, Atlantic Multi-decadal Oscillation index, and NINO 3.4 were recognized as the effective signals on the drought event in Birjand. Based on the results from stepwise regression analysis and regarding the processor limitations, eight models were extracted for further processing by CANFIS. The metrics P-factor and D-factor were utilized for uncertainty analysis, based on the sequential uncertainty fitting algorithm. Sensitivity analysis showed that for all models, NINO indices and rainfall variable had the largest impact on network performance. In model 4 (as the model with the lowest error during training and testing processes), NINO 1 + 2(t-5) with an average sensitivity of 0.7 showed the highest impact on network performance. Next, the variables rainfall, NINO 1 + 2(t), and NINO 3(t-6) with the average sensitivity of 0.59, 0.28, and 0.28, respectively, could have the highest effect on network performance. The findings based on network performance metrics indicated that the global indices with a time lag represented a better correlation with El Niño Southern Oscillation (ENSO). Uncertainty analysis of the model 4 demonstrated that 68 % of the observed data were bracketed by the 95PPU and D-Factor value (0.79) was also within a reasonable range. Therefore, the fourth model with a combination of the input variables NINO 1 + 2 (with 5 months of lag and without any lag), monthly rainfall, and NINO 3 (with 6 months of lag) and correlation coefficient of 0.903 (between observed and simulated SPI) was selected as the most accurate model for drought forecasting using CANFIS in the climatic region of Birjand.

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Acknowledgments

The authors would like to thank the University of Birjand for financial support of this study. The corresponding contract number was 19587/د/1392.

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Authors and Affiliations

  1. Department of Watershed Management, Faculty of Natural Resources and Environment, University of Birjand, Birjand, Iran

    Hadi Memarian

  2. Department of Water Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran

    Mohsen Pourreza Bilondi

  3. Department of Statistics, Faculty of Mathematics and Statistics, University of Birjand, Birjand, Iran

    Majid Rezaei

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  1. Hadi Memarian
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  2. Mohsen Pourreza Bilondi
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  3. Majid Rezaei
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Correspondence to Hadi Memarian.

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Memarian, H., Pourreza Bilondi, M. & Rezaei, M. Drought prediction using co-active neuro-fuzzy inference system, validation, and uncertainty analysis (case study: Birjand, Iran). Theor Appl Climatol 125, 541–554 (2016). https://doi.org/10.1007/s00704-015-1532-9

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