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Runoff Prediction Using a Novel Hybrid ANFIS Model Based on Variable Screening

Author

Listed:
  • Zhennan Liu

    (Guizhou Institute of Technology)

  • Qiongfang Li

    (Hohai University)

  • Jingnan Zhou

    (Guizhou Institute of Technology)

  • Weiguo Jiao

    (Guizhou Institute of Technology)

  • Xiaoyu Wang

    (Anhui Agricultural University)

Abstract

The accurate and reliable prediction of future runoff is important to guarantee for strengthening water resource optimization and management. The novel contribution of this article is the development of a hybrid model (FWA-ANFIS), which is based on the improvement of the adaptive neuro-fuzzy inference system (ANFIS) with the fireworks algorithm (FWA). The dominant driving factors of runoff are selected from several hydro-meteorological indices (precipitation, soil moisture content, and evaporation) as predictors by correlation coefficient (CC) analysis, mutual information (MI) analysis, correlation analysis and principal component analysis (CC-PCA), mutual information and kernel principal component analysis (MI-KPCA), MI-PCA, and CC-KPCA. The FWA-ANFIS model is applied to the Beiru River, China, with data from 1985–2016 (1985–2012 for model training and 2013–2016 for model prediction). The standard ANFIS, the GA-ANFIS, the PSO-ANFIS, the FWA-ELM, the GA-ELM, and the PSO-ELM are utilized as compared prediction models on the identical dataset. The results indicate that CC-PCA outperforms the other methods regarding the selection of predictors, and FWA-ANFIS has the best performance in terms of the root mean square error, correlation coefficient, and coefficient of determination, followed by the GA-ANFIS, PSO-ANFIS, ANFIS, FWA-ELM, GA-ELM, and PSO-ELM models. Furthermore, the degrees of uncertainty of the models increase in the following order: FWA-ANFIS, GA-ANFIS, PSO-ANFIS, ANFIS, PSO-ELM, GA-ELM, and FWA-ELM.

Suggested Citation

  • Zhennan Liu & Qiongfang Li & Jingnan Zhou & Weiguo Jiao & Xiaoyu Wang, 2021. "Runoff Prediction Using a Novel Hybrid ANFIS Model Based on Variable Screening," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2921-2940, July.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:9:d:10.1007_s11269-021-02878-4
    DOI: 10.1007/s11269-021-02878-4
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    References listed on IDEAS

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    Cited by:

    1. He, Chaofei & Chen, Fulong & Long, Aihua & Qian, YuXia & Tang, Hao, 2023. "Improving the precision of monthly runoff prediction using the combined non-stationary methods in an oasis irrigation area," Agricultural Water Management, Elsevier, vol. 279(C).

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