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Research On Urban Sprawl Boundary Based On The Distributed Cellular Automata And The BP Neural Network Water Quality Model

Posted on:2016-05-22Degree:MasterType:Thesis
Country:ChinaCandidate:X NingFull Text:PDF
GTID:2271330503456319Subject:Environmental Science and Engineering
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Urban sprawl may cause changes in regional land-use cover. With the increase of urban non-point pollution, it produces a complex and long-term adverse effects to the watershed. To simulate the change of urban land-use, and predict the response of the water quality upstream and downstream to the pollution, so that the quantitative relation between urban sprawl and water quality can be get. Then using the water quality standards to find the safe distance along the river between different cities, it will make big sense for the control of the risk in the watershed.Based on urban land-use change, this study used distributed cellular automata and BP neural network to build the model, which can simulate land-use change and water quality response. Achieving mathematical expressions of driving and constriction factors of land-use change, and dividing the region into several sub-regions as hydrological models do. The parameters in different sub-regions are different from each other. Genetic algorithm was use here to do parameter identification. Then the study can get different controlling factors in different sub-region by the spatial sensitivity analysis. On the need of BP neural network, generalizing the river and land-use data. To build another model which can simulate water quality response to the location and quantity of emissions. Analyzing the effect of urban space on water quality, and to get the safe distance using the water quality standard.Taking Wuhu and Ma’anshan together as a case study. Simulating the land-use change process and water quality in this region. The results showed that, the distance between the built-up areas are narrowing. The emission coming from Wuhu had a bad effect on the water quality in Ma’anshan. The concentrations of COD and NH4-N are 12.83 mg/L and 0.31mg/L, with a growth of 29.2% and 23.2% compared to those in 2010. It increased faster the emissions. To ensure the safety of the drinking water quality in Ma’anshan, it is supported that distance between two built-up areas should keep a safe distance of 3.17 km at least.
Keywords/Search Tags:Urban land use change, Distributed cellular automata, Genetic algorithm, BP neural network, Safe distance
PDF Full Text Request
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