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http://dx.doi.org/10.17820/eri.2020.7.2.114

Applicability Evaluation for Discharge Model Using Curve Number and Convolution Neural Network  

Song, Chul Min (Department of Policy for Watershed Management, The Policy Council for Paldang Watershed)
Lee, Kwang Hyun (Department of Policy for Watershed Management, The Policy Council for Paldang Watershed)
Publication Information
Ecology and Resilient Infrastructure / v.7, no.2, 2020 , pp. 114-125 More about this Journal
Abstract
Despite the various artificial neural networks that have been developed, most of the discharge models in previous studies have been developed using deep neural networks. This study aimed to develop a discharge model using a convolution neural network (CNN), which was used to solve classification problems. Furthermore, the applicability of CNN was evaluated. The photographs (pictures or images) for input data to CNN could not clearly show the characteristics of the study area as well as precipitation. Hence, the model employed in this study had to use numerical images. To solve the problem, the CN of NRCS was used to generate images as input data for the model. The generated images showed a good possibility of applicability as input data. Moreover, a new application of CN, which had been used only for discharge prediction, was proposed in this study. As a result of CNN training, the model was trained and generalized stably. Comparison between the actual and predicted values had an R2 of 0.79, which was relatively high. The model showed good performance in terms of the Pearson correlation coefficient (0.84), the Nash-Sutcliffe efficiency (NSE) (0.63), and the root mean square error (24.54 ㎥/s).
Keywords
Convolution neural network; Curve number; Deep learning; Discharge modeling; Linear function;
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