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期刊论文

A hybrid model to simulate the annual runoff of the Kaidu River in northwest China

Jianhua XuYaning ChenLing Bai

Hydrology and Earth System Sciences,2016,20(4):1447-1457 | 2016年04月16日 | 10.5194/hess-20-1447-2016

URL:https://www.hydrol-earth-syst-sci.net/20/1447/2016/

摘要/描述

Fluctuant and complicated hydrological processes can result in the uncertainty of runoff forecasting. Thus, it is necessary to apply the multi-method integrated modeling approaches to simulate runoff. Integrating the ensemble empirical mode decomposition (EEMD), the back-propagation artificial neural network (BPANN) and the nonlinear regression equation, we put forward a hybrid model to simulate the annual runoff (AR) of the Kaidu River in northwest China. We also validate the simulated effects by using the coefficient of determination (R2) and the Akaike information criterion (AIC) based on the observed data from 1960 to 2012 at the Dashankou hydrological station. The average absolute and relative errors show the high simulation accuracy of the hybrid model. R2 and AIC both illustrate that the hybrid model has a much better performance than the single BPANN. The hybrid model and integrated approach elicited by this study can be applied to simulate the annual runoff of similar rivers in northwest China.

版权说明:以下全部内容由徐建华上传于   2018年06月26日 20时49分36秒,版权归本人所有。

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