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Article

Developing a Discharge Estimation Model for Ungauged Watershed Using CNN and Hydrological Image

by 1 and 2,*
1
Department of Agricultural Rural Engineering, Chungbuk National University, Cheongju 28644, Korea
2
Department of Policy for Watershed Management, The Policy Council for Paldang Watershed, Yangpyeong 12585, Korea
*
Author to whom correspondence should be addressed.
Water 2020, 12(12), 3534; https://doi.org/10.3390/w12123534
Received: 4 November 2020 / Revised: 7 December 2020 / Accepted: 11 December 2020 / Published: 16 December 2020
(This article belongs to the Section Hydrology)
This study aimed to estimate the discharge in ungauged watersheds. To this end, we herein deviated from the model development methodology of previous studies and used convolution neural network (CNN), a deep training algorithm, and hydrological images. As the CNN model was developed for solving classification issues in general, it is unsuitable for simulating the discharge, which is a continuous variable. Therefore, the fully connected layer of the CNN model was improved. Moreover, images reflecting the hydrological conditions rather than a general photograph were used as input data for the CNN model. Three study areas that have discharge gauged data were set for the model’s training and testing. The data from two of the three study areas were used for CNN model training, and the data of the other were used to evaluate model prediction performance. The results of this study demonstrate a moderate predictive success of the discharge of an ungauged watershed using the CNN model and hydrological images. Therefore, it can be suitable as a methodology for the discharge estimation of ungauged watersheds. Simultaneously, it is expected that our methodology can be applied to the field of remote sensing or to the field of real-time discharge simulation using satellite imagery on a global scale or across a wide area. View Full-Text
Keywords: ungauged watershed; discharge; convolution neural network; hydrological image; curve number ungauged watershed; discharge; convolution neural network; hydrological image; curve number
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MDPI and ACS Style

Kim, D.Y.; Song, C.M. Developing a Discharge Estimation Model for Ungauged Watershed Using CNN and Hydrological Image. Water 2020, 12, 3534. https://doi.org/10.3390/w12123534

AMA Style

Kim DY, Song CM. Developing a Discharge Estimation Model for Ungauged Watershed Using CNN and Hydrological Image. Water. 2020; 12(12):3534. https://doi.org/10.3390/w12123534

Chicago/Turabian Style

Kim, Da Y., and Chul M. Song 2020. "Developing a Discharge Estimation Model for Ungauged Watershed Using CNN and Hydrological Image" Water 12, no. 12: 3534. https://doi.org/10.3390/w12123534

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