长江科学院院报Issue(7)：15-21,7.DOI:10.3969/j.issn.1001-5485.2015.07.004
基于加权灰色－马尔可夫链模型的城市需水预测
Prediction of Urban Water Demand by Using Weighted Grey-Markov Chain Model
摘要
Abstract
Forecast of urban water demand is a basic content of optimal allocation and planning for regional water re-sources.On the basis of gray GM(1,1)model of trend prediction,the weighted Markov chain prediction method is introduced to establish a weighted grey Markov GM(1,1 )model for predicting urban water demand.This model combines the feature of dealing with numbers of strong monotonous series with the feature of random wave response in extracting relative residuals through state transfer probability matrix.The model is applied to the prediction of ur-ban water demand in Chengdu city and the result indicates that the weighted grey Markov GM(1,1)model makes full use of the information given by urban water demand sequence and forecasts the transfer regularity of relative re-siduals sequence among system states,by which it enhances the precision correcting value of grey model prediction. Furthermore we compared this model with other two grey models and the prediction result suggests that the weighted gray Markov GM(1,1)model has higher accuracy.The urban water demand forecast in 2012 and 2013 is 742. 5091 million m3 and 798.1834 million m3 in Chengdu city respectively,presenting a significant increasing trend. Therefore this model improves the accuracy when dealing with stochastic fluctuating data,and broadens the applica-tion scope of grey model prediction and makes it more scientific.关键词
城市需水预测/GM(1,1)模型/加权马尔可夫链/转移概率/预测精度Key words
urban water demand prediction/GM(1,1 )model/weighted Markov chain/transfer probability/pre-diction accuracy分类
建筑与水利引用本文复制引用
杨皓翔,梁川,崔宁博..基于加权灰色－马尔可夫链模型的城市需水预测[J].长江科学院院报,2015,(7):15-21,7.基金项目
国家自然科学基金资助项目（51009101）；四川省软科学计划项目（2015ZR0157）；南方丘区节水农业研究四川省重点实验室开放基金 （）
