干旱区生态环境知识资源中心(Arid): Simulation and prediction of monthly accumulated runoff, based on several neural network models under poor data availability

Arid

DOI	10.3724/SP.J.1226.2018.00468
	Simulation and prediction of monthly accumulated runoff, based on several neural network models under poor data availability
	Qian, JianPing; Zhao, JianPing; Liu, Yi; Feng, XinLong; Gui, DongWei
通讯作者	Gui, DW
来源期刊	SCIENCES IN COLD AND ARID REGIONS
ISSN	1674-3822
出版年	2018
卷号	10 期号:6 页码:468-481
英文摘要	Most previous research on areas with abundant rainfall shows that simulations using rainfall-runoff modes have a very high prediction accuracy and applicability when using a back-propagation (BP), feed-forward, multilayer perceptron artificial neural network (ANN). However, in runoff areas with relatively low rainfall or a dry climate, more studies are needed. In these areas-of which oasis-plain areas are a particularly good example-the existence and development of runoff depends largely on that which is generated from alpine regions. Quantitative analysis of the uncertainty of runoff simulation under climate change is the key to improving the utilization and management of water resources in arid areas. Therefore, in this context, three kinds of BP feed-forward, three-layer ANNs with similar structure were chosen as models in this paper. Taking the oasis-plain region traverse by the Qira River Basin in Xinjiang, China, as the research area, the monthly accumulated runoff of the Qira River in the next month was simulated and predicted. The results showed that the training precision of a compact wavelet neural network is low; but from the forecasting results, it could be concluded that the training algorithm can better reflect the whole law of samples. The traditional artificial neural network (TANN) model and radial basis-function neural network (RBFNN) model showed higher accuracy in the training and prediction stage. However, the TANN model, more sensitive to the selection of input variables, requires a large number of numerical simulations to determine the appropriate input variables and the number of hidden-layer neurons. Hence, The RBFNN model is more suitable for the study of such problems. And it can be extended to other similar research arid-oasis areas on the southern edge of the Kunlun Mountains and provides a reference for sustainable water-resource management of arid-oasis areas.
英文关键词	oasis artificial neural network radial basis function wavelet function runoff simulation
类型	Article
语种	英语
收录类别	ESCI
WOS记录号	WOS:000457535700003
WOS关键词	WATER-RESOURCES APPLICATIONS ; TARIM RIVER-BASIN ; INPUT DETERMINATION ; ECOSYSTEM SERVICES ; BAYESIAN NETWORK ; CLIMATE-CHANGE ; WAVELET ; ANN ; QUANTIFICATION ; PRECIPITATION
WOS类目	Geography, Physical
WOS研究方向	Physical Geography
来源机构	中国科学院新疆生态与地理研究所 ; 新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/333278
作者单位	[Qian, JianPing; Zhao, JianPing; Feng, XinLong] Xinjiang Univ, Coll Math & Syst Sci, Urumqi 830046, Xinjiang, Peoples R China; [Liu, Yi; Gui, DongWei] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Xinjiang 830011, Peoples R China; [Liu, Yi; Gui, DongWei] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, Cele Natl Stn Observat & Res Desert Grassland Eco, Urumqi 830011, Xinjiang, Peoples R China
推荐引用方式 GB/T 7714	Qian, JianPing,Zhao, JianPing,Liu, Yi,et al. Simulation and prediction of monthly accumulated runoff, based on several neural network models under poor data availability[J]. 中国科学院新疆生态与地理研究所, 新疆大学,2018,10(6):468-481.
APA	Qian, JianPing,Zhao, JianPing,Liu, Yi,Feng, XinLong,&Gui, DongWei.(2018).Simulation and prediction of monthly accumulated runoff, based on several neural network models under poor data availability.SCIENCES IN COLD AND ARID REGIONS,10(6),468-481.
MLA	Qian, JianPing,et al."Simulation and prediction of monthly accumulated runoff, based on several neural network models under poor data availability".SCIENCES IN COLD AND ARID REGIONS 10.6(2018):468-481.