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The focuses of this thesis is to develop a comprehensive system named as GIS-SWQAM, which includes: (1) the development of a GIS-based water quality assessment system to assess the water quality and provide spatial distribution of water quality variables (2) the development of an artificial neural network model to predict the change of water quality variables (3) the development of a user interface that integrates the above models and functions furthermore, a comparative analysis of the modeling approach developed in the GIS-SWQAM and the commercial model MIKE 21 was performed through field case studies. The emergence of spatial information technologies, such as Geographic Information System (GIS) make it possible to assess and predict surface water quality with more details with respect to spatial information. Water quality models are useful tools to simulate the complex transport and fate of pollutants in a water body and predict the short-term and long-term effects on water quality variation. The adverse health and environmental effects of surface water pollution have been a major concern in environmental management.
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It is an fact that surface water receives a large volume of pollutants from industrial, agricultural, and municipal sources.
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