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

	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
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	CSCD
WOS研究方向	Geology
CSCD记录号	CSCD:6406888
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/336053
作者单位	Qian Jianping, College of Mathematics and System Sciences, Xinjiang University, Urumqi, Xinjiang 830046, China.; Zhao Jianping, College of Mathematics and System Sciences, Xinjiang University, Urumqi, Xinjiang 830046, China.; Feng Xinlong, College of Mathematics and System Sciences, Xinjiang University, Urumqi, Xinjiang 830046, China.; Liu Yi, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences;;Cele National Station of Observation and Research for Desert-Grassland Ecosystem, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, State Key Laboratory of Desert and Oasis Ecology;;, ;;Urumqi, Xinjiang;;Xinjiang 830011;;830011.; Gui Dongwei, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences;;Cele National Station of Observation and Research for Desert-Grassland Ecosystem, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, State Key Laboratory of Desert and Oasis Ecology;;, ;;Urumqi, Xinjiang;;Xinjiang 830011;;830011.
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],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.