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Establishment And Application Of The Interactive Model Of The Urban Water Demand Prediction

Posted on:2007-07-13Degree:MasterType:Thesis
Country:ChinaCandidate:S P GengFull Text:PDF
GTID:2132360185951940Subject:Hydrology and water resources
Abstract/Summary:PDF Full Text Request
In this research work, Authors collected and sorted of the primary datum of water consumption, population, GDP, Output Value of every industry and water use quotas in the studied area, analyzed the tendency of water consumption. Combined with it in developed countries and the National Conditions of China to grasp the objective law of water consumption increasing. Based on the difference of forecasting objects, choosing mechanism of water demand, the tendency of water requirement which agree well with the practical results .exploring the prediction method based on the collected datum and make maximum use of. Using Both Judgment and Statistical Forecasting Methods and establish an interactive model of water demand in city. Applying historical data and combined with the historical simulation of sample datum, it also compared the predicting numerical value with actual numerical value, analyzed forecast deviation, modified and adjusted the model parameters to avoid the accumulation of rounding errors.The model structure can be fall into four modules: Gross Domestic Product prediction module, water withdrawal quato prediction module, the water demand prediction module. Input the collected datum into every module, compare forecast results and the tendency of sample datum, then adjust and rate the parameter of the model, sensitivity analysis. Finally input the rated parameters into model, predict water demand in research area and analyze rationality, output the forecast results. The model is based on the plat roof of EXCEL and achieved, the practicability and adaptability are better. The results show that it is a useful means to apply the model in Urumqi.
Keywords/Search Tags:interactive, the urban water demand, predication model, submodule
PDF Full Text Request
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