Combining BPANN and wavelet analysis to simulate hydro-climatic processes----a case study of the Kaidu River, North-west China

doi:10.1007/s11707-013-0354-2

Front Earth Sci

2013, Vol. 7

Issue (2) : 227-237 https://doi.org/10.1007/s11707-013-0354-2

RESEARCH ARTICLE

Combining BPANN and wavelet analysis to simulate hydro-climatic processes----a case study of the Kaidu River, North-west China

Jianhua XU¹(

), Yaning CHEN², Weihong LI², Paul Y. PENG³, Yang YANG¹, Chu’nan SONG¹, Chunmeng WEI¹, Yulian HONG¹

1. 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; 2. The Key Laboratory of Oasis Ecology and Desert Environment, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; 3. Department of Community Health and Epidemiology, Queen’s University, Kingston, K7L 3N6, Canada

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Abstract

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.

Keywords hydro-climatic process Kaidu River simulation wavelet analysis (WA) back-propagation artificial neural network (BPANN) multiple linear regression (MLR)

Corresponding Author(s): XU Jianhua,Email:jhxu@geo.ecnu.edu.cn

Issue Date: 05 June 2013

Cite this article:

Jianhua XU,Yaning CHEN,Weihong LI, et al. Combining BPANN and wavelet analysis to simulate hydro-climatic processes----a case study of the Kaidu River, North-west China[J]. Front Earth Sci, 2013, 7(2): 227-237.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-013-0354-2
https://academic.hep.com.cn/fesci/EN/Y2013/V7/I2/227

Fig.1 The location of the study area

Fig.2 Operation of a typical back-propagation artificial neural network

Fig.3 The back-propagation training process

Fig.4 Variation patterns of AAT and AP at different time scales

Tab.1 Basic parameters of the BPANN for hydro-climatic process at different time scales

Tab.2 MLREs for hydro-climatic process at different time scales

Fig.5 Simulated results for AR by BPANN and MLR at the different time scales

Tab.3 Comparison between BPANN and MLR model at different time scales

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