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Multi-Objective Optimization for Flood Interval Prediction Based on Orthogonal Chaotic NSGA-II and Kernel Extreme Learning Machine

Author

Listed:
  • Tian Peng

    (Huaiyin Institute of Technology
    Huazhong University of Science and Technology)

  • Chu Zhang

    (Huaiyin Institute of Technology
    Huazhong University of Science and Technology)

  • Jianzhong Zhou

    (Huazhong University of Science and Technology)

  • Xin Xia

    (Huaiyin Institute of Technology)

  • Xiaoming Xue

    (Huaiyin Institute of Technology)

Abstract

Deterministic flood prediction methods can only provide future point prediction results of the target variable. The intrinsic uncertainties and the fluctuation range of the prediction results cannot be evaluated. This study proposes a flood interval prediction method based on orthogonal chaotic non-dominated sorting genetic algorithm-II (OCNSGA-II) and kernel extreme learning machine (KELM) to estimate the uncertainty of the flood prediction results. The dual-output KELM model is exploited to predict the upper and lower bounds of the possible flood prediction result. The OCNSGA-II algorithm is employed to adjust the hidden layer output weights of the KELM model to minimize the prediction interval normalized average width (PINAW) and maximize the prediction interval coverage probability (PICP). The target variable with a disturbance of ±10% are taken as the initial upper and lower bounds. The superiority of the proposed method has been validated on one a real-world data set collected from the upper reaches of the Yangtze River in China. Results have shown that the proposed model can obtain prediction intervals with higher quality than the conventional single-objective interval prediction models and the other multi-objective benchmark models.

Suggested Citation

  • Tian Peng & Chu Zhang & Jianzhong Zhou & Xin Xia & Xiaoming Xue, 2019. "Multi-Objective Optimization for Flood Interval Prediction Based on Orthogonal Chaotic NSGA-II and Kernel Extreme Learning Machine," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4731-4748, November.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:14:d:10.1007_s11269-019-02387-5
    DOI: 10.1007/s11269-019-02387-5
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    References listed on IDEAS

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    1. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Xiao-Yun Chen, 2015. "Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2655-2675, June.
    2. Chu Zhang & Tian Peng & Chaoshun Li & Wenlong Fu & Xin Xia & Xiaoming Xue, 2019. "Multiobjective Optimization of a Fractional-Order PID Controller for Pumped Turbine Governing System Using an Improved NSGA-III Algorithm under Multiworking Conditions," Complexity, Hindawi, vol. 2019, pages 1-18, February.
    3. Tian Peng & Jianzhong Zhou & Chu Zhang & Na Sun, 2018. "Modeling and Combined Application of Orthogonal Chaotic NSGA-II and Improved TOPSIS to Optimize a Conceptual Hydrological Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(11), pages 3781-3799, September.
    4. Hairong Zhang & Jianzhong Zhou & Lei Ye & Xiaofan Zeng & Yufan Chen, 2015. "Lower Upper Bound Estimation Method Considering Symmetry for Construction of Prediction Intervals in Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5505-5519, December.
    5. Sanjeet Kumar & Mukesh Tiwari & Chandranath Chatterjee & Ashok Mishra, 2015. "Reservoir Inflow Forecasting Using Ensemble Models Based on Neural Networks, Wavelet Analysis and Bootstrap Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4863-4883, October.
    6. Wei Li & Jianzhong Zhou & Huaiwei Sun & Kuaile Feng & Hairong Zhang & Muhammad Tayyab, 2017. "Impact of Distribution Type in Bayes Probability Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(3), pages 961-977, February.
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    Cited by:

    1. Xinyu Chang & Jun Guo & Hui Qin & Jingwei Huang & Xinying Wang & Pingan Ren, 2024. "Single-Objective and Multi-Objective Flood Interval Forecasting Considering Interval Fitting Coefficients," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(10), pages 3953-3972, August.

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