Some types of land development can be associated with increased impervious area that causes increase\nin surface runoff and decrease in ground water recharge. Both of these processes can have large-scale\nramifications through time. Increased runoff results in higher flows during rainfall events. On the other\nhand, groundwater recharge decreases due to increase impervious surfaces and decrease rate. Hence,\nthere is a need to quantify the impacts of landuse changes from the point of minimizing potential\nenvironmental degradation. The objective of this study is to develop a model for assessing the impacts\non the watershed runoff due to changes in landscape patterns. While conceptual or physical based\nmodels are of importance in the understanding of hydrologic processes, there are many practical\nsituations where the main concern is with making accurate predictions at specific locations. For this\npurpose, artificial neural network (ANN) model was developed. Landsat data was used in this study in\nview of its ability to provide useful information on landuse dynamics. The model�s performance in both\ntraining and testing phases were evaluated using mean absolute error (MAE), mean square error (MSE),\nU Theil�s coefficient and regression analysis. The correlation coefficients between simulated and real\ndata were found to be 0.94 and 0.89 for the training and testing phases respectively. Most of the data\npoints were within the confidence level of 95%. The model can be used as a decision making tool when\nformulating landuse policies. It can be a practical tool for hydrologists, engineers, and town and country\nplanners.
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