The Annual Maximum Flood Peak Discharge Forecasting Using Hermite Projection Pursuit Regression with SSO and LS Method

Wen-chuan Wang (), Kwok-wing Chau (), Dong-mei Xu (), Lin Qiu () and Can-can Liu
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Wen-chuan Wang: North China University of Water Resources and Electric Power
Kwok-wing Chau: Hong Kong Polytechnic University, Hung Hom
Dong-mei Xu: North China University of Water Resources and Electric Power
Lin Qiu: North China University of Water Resources and Electric Power
Can-can Liu: University College London

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2017, vol. 31, issue 1, No 29, 477 pages

Abstract: Abstract Accurate prediction of extreme flood peak discharge is essential in developing the best management practices to avoid and reduce flood disaster. In recent years, many techniques have been pronounced as a branch of computer science to model wide range of hydrological process. Nevertheless, exploration of more efficient technique is necessary in terms of accuracy and applicability. In this study, a novel hermite-PPR model with SSO and LS algorithm is proposed for designing annual maximum flood peak discharge forecasting model at Yichang station on Yangtze River in China. The statistical properties of the data series are utilized for identifying an appropriate input vector to the model and then the performance of the proposed models were compared with adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and multiple linear regression (MLR) methods in terms of root mean squared error (RMSE), mean absolute relative error (MARE), coefficient of correlation (CC), Nash-Sutcliffe efficiency coefficient (NSEC) and qualified rate (QR). The results indicate that the presented methodology in this research can obtain significant improvement in forecasting accuracy in terms of different evaluation criteria during training and validation phases.

Keywords: Annual maximum flood peak; Flood forecasting; Hermite polynomial; Projection pursuit regression; Social spider optimization; Least square method; Artificial neural network (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11269-016-1538-9

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