您当前所在位置: 首页 > 学者

徐建华

  • 50浏览

  • 0点赞

  • 0收藏

  • 0分享

  • 0下载

  • 0评论

  • 引用

期刊论文

Integrating Wavelet Analysis and BPANN to Simulate the Annual Runoff With Regional Climate Change: A Case Study of Yarkand River, Northwest China

Jianhua XuYaning ChenWeihong LiQin Nie

Water Resources Management,2014,28(9):92523–2537 | 2014年07月18日 | 10.1007/s11269-014-0625-z

URL:https://link.springer.com/article/10.1007/s11269-014-0625-z

摘要/描述

Selecting the Yarkand River as a typical representative of an inland river in northwest China, We identified the variation pattern of hydro-climatic process based on the hydrological and meteorological data during the period of 1957 ~ 2008, and constructed an integrated model to simulate the change of annual runoff (AR) with annual average temperature (AAT) and annual precipitation (AP) by combining wavelet analysis (WA) and artificial neural network (ANN) at different time scale. The results showed that the pattern of hydro-climatic process is scale-dependent in time. At 16-year and 32-year time scale, AR presents a monotonically increasing trend with the similar trend of AAT and AP. But at 2-year, 4-year, and 8-year time scale, AR exhibits a nonlinear variation with fluctuations of AAT and AP. The back propagation artificial neural network based on wavelet decomposition (BPANNBWD) well simulated the change of AR with AAT and AP at the all five time scales. Compared to the traditional statistics model, the simulation effect of BPANNBWD is better than that of multiple linear regression (MLR) at every time scale. The results also revealed the fact that the simulation effect at a larger time scale (e.g. 16-year or 32-year scale) is better than that at a smaller time scale (e.g. 2-year or 4-year scale).

版权说明:以下全部内容由徐建华上传于   2018年06月26日 21时07分39秒,版权归本人所有。

我要评论

全部评论 0

本学者其他成果

    同领域成果