Combining BPANN and wavelet analysis to simulate hydro-climatic processes—a case study of the Kaidu River, North-west China
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- 作者：Jianhua Xu (1354)
Yaning Chen (2354)
Weihong Li (2354)
Paul Y. Peng (3354)
Yang Yang (1354)
Chu’nan Song (1354)
Chunmeng Wei (1354)
Yulian Hong (1354) - 关键词：hydro ; climatic process ; Kaidu River ; simulation ; wavelet analysis (WA) ; back ; propagation artificial neural network (BPANN) ; multiple linear regression (MLR)
- 刊名：Frontiers of Earth Science
- 出版年：2013
- 出版时间：June 2013
- 年：2013
- 卷：7
- 期：2
- 页码：227-237
- 全文大小：323KB
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Yaning Chen (2354)
Weihong Li (2354)
Paul Y. Peng (3354)
Yang Yang (1354)
Chu’nan Song (1354)
Chunmeng Wei (1354)
Yulian Hong (1354)
1354. The Key Laboratory of GIScience of the Education Ministry of China, The Research Center for East-West Cooperation in China, East China Normal University, Shanghai, 200241, China
2354. The Key Laboratory of Oasis Ecology and Desert Environment, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
3354. Department of Community Health and Epidemiology, Queen’s University, Kingston, K7L 3N6, Canada - ISSN：2095-0209
文摘
Using the hydrological and meteorological data in the Kaidu River Basin during 1957-008, 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 hydroclimatic process based on WA at each time scale, but the simulated effect from BPANN is better than that from MLR.