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

Tian Peng, Chu Zhang (), Jianzhong Zhou, Xin Xia and Xiaoming Xue
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Tian Peng: Huaiyin Institute of Technology
Chu Zhang: Huaiyin Institute of Technology
Jianzhong Zhou: Huazhong University of Science and Technology
Xin Xia: Huaiyin Institute of Technology
Xiaoming Xue: Huaiyin Institute of Technology

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2019, vol. 33, issue 14, No 5, 4748 pages

Abstract: 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.

Keywords: Flood interval prediction; Uncertainty analysis; Multi-objective optimization; Orthogonal chaotic NSGA-II; Kernel extreme learning machine (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11269-019-02387-5

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