Integrating Artificial Neural Networks into the VIC Model for Rainfall-Runoff Modeling
AbstractA hybrid rainfall-runoff model was developed in this study by integrating the variable infiltration capacity (VIC) model with artificial neural networks (ANNs). In the proposed model, the prediction interval of the ANN replaces separate, individual simulation (i.e., single simulation). The spatial heterogeneity of horizontal resolution, subgrid-scale features and their influence on the streamflow can be assessed according to the VIC model. In the routing module, instead of a simple linear superposition of the streamflow generated from each subbasin, ANNs facilitate nonlinear mappings of the streamflow produced from each subbasin into the total streamflow at the basin outlet. A total of three subbasins were delineated and calibrated independently via the VIC model; daily runoff errors were simulated for each subbasin, then corrected by an ANN bias-correction model. The initial streamflow and corrected runoff from the simulation for individual subbasins serve as inputs to the ANN routing model. The feasibility of this proposed method was confirmed according to the performance of its application to a case study on rainfall-runoff prediction in the Jinshajiang River Basin, the headwater area of the Yangtze River. View Full-Text
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Meng, C.; Zhou, J.; Tayyab, M.; Zhu, S.; Zhang, H. Integrating Artificial Neural Networks into the VIC Model for Rainfall-Runoff Modeling. Water 2016, 8, 407.
Meng C, Zhou J, Tayyab M, Zhu S, Zhang H. Integrating Artificial Neural Networks into the VIC Model for Rainfall-Runoff Modeling. Water. 2016; 8(9):407.Chicago/Turabian Style
Meng, Changqing; Zhou, Jianzhong; Tayyab, Muhammad; Zhu, Shuang; Zhang, Hairong. 2016. "Integrating Artificial Neural Networks into the VIC Model for Rainfall-Runoff Modeling." Water 8, no. 9: 407.
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