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Combining BPANN and wavelet analysis to simulate hydro-climatic processes a case study of the Kaidu River, North-west China 被引量:4
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作者 Jianhua XU Yaning CHEN +5 位作者 Weihong LI Paul Y. PENG Yang YANG Chunan SONG Chunmeng-WEI Yulian HONG 《Frontiers of Earth Science》 SCIE CAS CSCD 2013年第2期227-237,共11页
Using the hydrological and meteorological data in the Kaidu River Basin during 1957-2008, we simulated the hydro-climatic process by back-propagation artificial neural network (BPANN) based on wavelet analysis (WA... Using the hydrological and meteorological data in the Kaidu River Basin during 1957-2008, we simulated the hydro-climatic process by back-propagation artificial neural network (BPANN) based on wavelet analysis (WA), and then compared the simulated results with those from a multiple linear regression (MLR). The results show that the variation of runoff responded to regional climate change. The annual runoff (AR) was mainly affected by annual average temperature (AAT) and annual precipitation (AP), which revealed different varia- tion patterns at five time scales. At the time scale of 32-years, AR presented a monotonically increasing trend with the similar trend of AAT and AP. But at the 2-year, 4- year, 8-year, and 16-year time-scale, AR presented non-linear variation with fluctuations of AAT and AP. Both MLR and BPANN successfully simulated the hydro- climatic process based on WA at each time scale, but the simulated effect from BPANN is better than that from MLR. 展开更多
关键词 hydro-climatic process Kaidu River simulation wavelet analysis (WA) back-propagation artificial neural network (BPANN) multiple linear regression (MLR)
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