干旱区生态环境知识资源中心(Arid): 城市需水量模拟及不确定性分析方法研究

Arid

	城市需水量模拟及不确定性分析方法研究
其他题名	Method of simulation and uncertainty analysis for urban water demand
	聂思雨 1; 莫淑红 1; 王学凤 2; 沈冰 1
来源期刊	干旱区地理
ISSN	1000-6060
出版年	2015
卷号	38 期号:2 页码:260-266
中文摘要	需水量预测是区域水资源合理配置和有效管理的基础。由于供需水量的不确定性,精准的需水量预测较有困难。通过建立径向基函数(RBF)与BP神经网络预测模型,以西北地区城市西安市需水量为例,将用水量驱动因子作为模型输入,利用1990-2009年20组年用水数据进行网络训练模拟,对2010-2012年3组年用水数据进行检验预测,采用不确定性评价指数和置信区间方法对两种模型及模拟结果进行比较分析。结果表明,RBF与BP模型预测期的均方根误差分别为0.08、0.26,不确定性评价指数分别为0.74、1.02且BP模型预测值的相对误差最大超过10%以上,而RBF模型预测值的相对误差均小于5%,说明RBF模型模拟效果好,具有预测精度高以及不确定性影响低的双重优势;在模拟结果基础上,引用置信区间分析了结果的可靠性,为分析城市需水预测的不确定性提供了依据。
英文摘要	Water demand forecast is the foundation of rational allocation and effective manage for regional water resources. Commonly,there are mainly various methods for water demand forecasting in urban, for example,quota, grey theory, artificial neural network and so on. Various forecasting methods have their own advantages and disadvantages. It is not ideal for traditional prediction method to predict accurately, such as the gray theory. However, artificial neural network model has the advantages of strong generalization ability and stable algorithm. The present study shows that no matter what kind of model, the result of prediction is described by using a single numerical points, which represent the future time requirement water, ignoring the water randomness and uncertainty of model. It is difficult to calculate the accurate water in the future, because the water system in urban is complex, which consist of various factors. Therefore, in this paper, Radial Basis Function (RBF) and Back-Propagation (BP) neural network prediction model are developed to simulate and forecast water demand in Xi’an City, Shaanxi Province,China as an example,where is the semi-arid region in the northwest China and has the serious contradiction between supply and demand. Six key factors which related to water demand are inputs for both models, and water demand is outputs. 20 groups of data from 1990-2009 are used for training the networks and three groups of data from 2010-2012 are used for testing. The results show that the root mean square error (RMSE) of RBF and BP are 0.223 and 0.206 respectively in the period of 1990-2009, and 0.08 and 0.26 for those in the period of 2010-2012. The relative errors of RBF are all less than 5%, while the maximum relative error of BP is more than 10%. It makes clear that the simulation and prediction results of RBF neural network are more accurate than those of BP. Furthermore, the evaluation index (d-factor) and confidence intervals are calculated to measure and compare the uncertainty related to the outputs of the two models. The results show that the d -factor is 0.74 and 1.02 for RBF and BP separately which means the RBF model has less uncertainty than the BP model. Based on the results of simulation and forecasting,the 95% confidence interval is applied for analyzing the reliability of results, which can make the result in the special range change and provide the reliable water demand variation range in different situations. It is important significance for water resources management of Xi’an to get reference information from this method.
中文关键词	需水量 ; RBF神经网络 ; BP神经网络 ; 不确定性分析 ; 西安市
英文关键词	water demand RBF neural network BP neural network uncertainty analysis Xi’an City
语种	中文
国家	中国
收录类别	CSCD
WOS类目	ENGINEERING MULTIDISCIPLINARY
WOS研究方向	Engineering
CSCD记录号	CSCD:5398628
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/232644
作者单位	1.西安理工大学, 陕西省生态水利工程国家重点实验室培育基地, 西安, 陕西 710048, 中国; 2.中国水利水电科学研究院综合事业部, 北京 100038, 中国
推荐引用方式 GB/T 7714	聂思雨,莫淑红,王学凤,等. 城市需水量模拟及不确定性分析方法研究[J],2015,38(2):260-266.
APA	聂思雨,莫淑红,王学凤,&沈冰.(2015).城市需水量模拟及不确定性分析方法研究.干旱区地理,38(2),260-266.
MLA	聂思雨,et al."城市需水量模拟及不确定性分析方法研究".干旱区地理 38.2(2015):260-266.