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Evaluating the impacts of land use on surface water quality using geographically weighted regression


Most traditional linear regression models ignore local variations of spatial data. In this study, a new technique called geographically weighted regression model (GWR) was introduced to evaluate the impacts of land use on surface water. The reason for the spatial variations of relationships between land use and water quality were explored. Meanwhile, the adjusted R2, Akaike information criterion (AICc) and spatial autocorrelation index (Moran's I) of residuals were compared with ordinary least squares model (OLS) to verify if the GWR model is better than OLS in the prediction accuracy and the capacity of conducting spatial autocorrelation. The results showed that impact of the same types of land use on water quality changes in direction or size along with the variation of spatial position. For example, the relationships between TN and agricultural land in Wen-Rui Tang River showed a positive correlation in countryside and negative correlation in urban area in GWR models. The absolute values of regression coefficients in old downtown area were higher than other places. In the GWR model of dissolved oxygen (DO) and population density, the relationships were negative in the whole study area, which was consistent with the OLS results, and the effect of population density on DO is greater in suburban and rural areas. The reason for these spatial changes over different sub-watersheds indicated that the changes of land use percentage and the varied main pollution sources are fundamental factors. Furthermore, the adjusted R2 and AICc values from the 80 established models confirmed that as a local statistical model, GWR had better prediction accuracy than OLS model and could better reflected the actual spatial characteristics.

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