Spatial Prediction of Dissolved Organic Carbon Using GIS and ANN Modeling in River Networks
Computational Intelligence and Security（2011）
摘要
That GIS-based hydrological response units (HRUs) incorporated watershed variables and their potential spatial correlation into ANN modeling was clarified in the paper. The process and final results of neural network modeling were both assessed by the deterministic or statistical methods, spatial regression kriging (RK). The relation of prediction errors and HRUs area scale can provide useful information to optimize the design of stream monitoring network. It is indicated that potential advantage of ANN for watershed and the assessment of estuarine river impacts can be done by precise spatial prediction and sensitive factors analysis.
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关键词
watershed variables,gis-based hydrological response units,dissolved organic carbon spatial prediction,river networks,statistical methods,stream monitoring network,precise spatial prediction,ann modeling,regression kriging(rk),geographic information systems,spatial prediction,spatial regression kriging,regression analysis,estuarine river impact assessment,rivers,artificial neural network (ann),hydrological response units (hrus),sensitive factors analysis,neural network modeling,dissolved organic carbon (doc),incorporated watershed variable,prediction error,environmental factors,deterministic methods,prediction errors,hrus area scale,spatial correlation,dissolved organic carbon,neural nets,potential spatial correlation,correlation methods,potential advantage,artificial neural networks,neural network,neural network model,factor analysis,correlation,artificial neural network,carbon
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