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干旱区地理 ›› 2015, Vol. 38 ›› Issue (2): 260-266.

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城市需水量模拟及不确定性分析方法研究

聂思雨1,莫淑红1,王学凤2,沈冰1   

  1. (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, 陕西    西安    710048;2    中国水利水电科学研究院综合事业部, 北京    100038)
  • 收稿日期:2014-06-18 修回日期:2014-10-15 出版日期:2015-03-25
  • 通讯作者: 莫淑红(1972-),女,博士,副教授. Email:moshuhong@xaut.edu.cn
  • 作者简介:聂思雨(1989-),女,黑龙江大兴安岭人,硕士,从事旱区水文水资源研究. Email:nsy727@126.com
  • 基金资助:

    国家自然科学基金项目(51209169)

Method of simulation and uncertainty analysis for urban water demand

NIE  Si-yu1,MO  Shu-hong1,WANG  Xue-feng2,SHEN  Bing1   

  1. (1    State Key Laboratory of Eco-Hydraulic Engineering in Shaanxi at XAUT,Xi’an  710048,Shaanxi,China;2    China Institute of Water Resources and Hydropower Research,Beijing  100038,China)
  • Received:2014-06-18 Revised:2014-10-15 Online:2015-03-25

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。

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

Abstract: 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. 搜索 #oXBr5L4gjSTips { Z-INDEX: 999999999; POSITION: absolute; WIDTH: 56px; HEIGHT: 24px; LEFT: 1342177.27em } #oXBr5L4gjSTips A { POSITION: relative; LINE-HEIGHT: 24px; MARGIN: -32px 0px 0px; PADDING-LEFT: 23px; WIDTH: auto; DISPLAY: block; BACKGROUND: url(http://mat1.gtimg.com/www/sogou/sogou_tips_v1.png) no-repeat 0px 0px; HEIGHT: 24px; COLOR: #000; FONT-SIZE: 12px; TEXT-DECORATION: none } #oXBr5L4gjSTips A:hover { BACKGROUND-POSITION: 0px -34px; COLOR: #45a1ea }

Key words: water demand, RBF neural network, BP neural network, uncertainty analysis, Xi’an City

中图分类号: 

  • TV213.4