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篇目详细内容 |
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【篇名】 |
Combining BPANN and wavelet analysis to simulate hydro-climatic processes----a case study of the Kaidu River, North-west China |
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【刊名】 |
Frontiers of Earth Science |
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【刊名缩写】 |
Front. Earth Sci |
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【ISSN】 |
2095-0195 |
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【EISSN】 |
2095-0209 |
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【DOI】 |
10.1007/s11707-013-0354-2 |
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【出版社】 |
Higher Education Press and Springer-Verlag Berlin
Heidelberg |
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【出版年】 |
2013 |
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【卷期】 |
7
卷2期 |
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【页码】 |
227-237
页,共
11
页 |
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【作者】 |
Jianhua XU;
Yaning CHEN;
Weihong LI;
Paul Y.;
PENG;
Yang YANG;
Chu’;
nan SONG;
Chunmeng WEI;
Yulian HONG;
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【关键词】 |
hydro-climatic process; Kaidu River; simulation; wavelet analysis (WA); back-propagation artificial neural network (BPANN); multiple linear regression (MLR) |
【摘要】 |
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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 variation 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 nonlinear 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. |
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