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

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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.

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Data Availability

The data that support the funding of this study are available from the first author upon reasonable request.

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Funding

This work is supported by the National Natural Science Foundation of China (No. 52069005), the Guizhou province science and technology fund (Guizhou Science Foundation-ZK[2021] General 295), and High-level Talents Start-up Fund Project of Guizhou Institute of Technology (XJGC20210425) and special thanks are given to the anonymous reviewers and editors for their constructive comments.

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Authors and Affiliations

  1. School of Civil Engineering, Guizhou Institute of Technology, Guiyang, 550003, China

    Zhennan Liu, Jingnan Zhou & Weiguo Jiao

  2. College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, China

    Qiongfang Li

  3. School of Engineering, Anhui Agricultural University, Hefei, 230036, China

    Xiaoyu Wang

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  1. Zhennan Liu
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  2. Qiongfang Li
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  4. Weiguo Jiao
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  5. Xiaoyu Wang
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Contributions

Zhennan Liu: Conceptualization, Methodology, Software. Qiongfang Li: Data curation, Writing- Original draft preparation. Jingnan Zhou: Writing- Reviewing and Editing. Weiguo Jiao: Software, Validation. Xiaoyu Wang: Visualization.

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Correspondence to Jingnan Zhou.

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Liu, Z., Li, Q., Zhou, J. et al. Runoff Prediction Using a Novel Hybrid ANFIS Model Based on Variable Screening. Water Resour Manage 35, 2921–2940 (2021). https://doi.org/10.1007/s11269-021-02878-4

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