基于联系隶属度的城市需水量预测模型

城市需水量预测是生态城市规划与管理的基础,但受诸多不确定因素影响,是一个复杂的预测难题。为能定量统一表达预测年份需水量各影响因素间及与历史数据间的交叉、交融的确定和不确定关系,在此应用有序聚类理论与集对分析的耦合方法,提出了基于联系隶属度概念的城市需水量预测模型。该模型首先基于城市需水量历史数据进行最优分割聚类,应用联系隶属度对预测年份需水量的影响因子与历史数据关系进行同异反分析,并构建相似模型预测相应年份的城市需水量。实例应用及与其他方法对比的结果表明,该模型应用于城市需水预测是有效可行的。

Forecast model of urban water demand based on connectional membership degree

The forecast of urban water demand is the basis of plan and management of ecological city. However, it is a complex problem affected by various uncertainty factors. In order to express the integration and blending relationships of certainty and uncertainty among influence factors and between samples and historical data as a whole,the paper used an orderly clustering theory and coupling method of set pair analysis to present a forecast model of urban water demand based on connectional membership degree. In the model, the ordered historical data of urban water demand of corresponding years was first clustered according to Fisher's optimal partition method, then the identical-discrepancy-contrary analysis was carried out to express the relationship between impact factors of one year and historical data. Moreover,it structured a similar model to forecast city water demand of corresponding year.The results from the practical example and comparative analyses with other methods show it is reasonable and effective for application of the model to prediction of city water demand.