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Original Article

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

Curve Number 및 Convolution Neural Network를 이용한 유출모형의 적용성 평가

Chul Min Song1*
,
Kwang Hyun Lee1
송 철민1*
,
이 광현1
1Researcher Fellow, Department of Policy for Watershed Management, The Policy Council for Paldang Watershed, Yangpyeong 12585, Korea
1특별대책지역 수질보전정책협의회 정책국 연구위원
* Corresponding Author
30 June 2020. pp. 114-125
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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 m3/s).

Keywords
Convolution neural network
Curve number
Deep learning
Discharge modeling
Linear function

본 연구는 유출모형 연구를 위해 주로 사용되었던 DNN에서 벗어나, 다양한 신경망을 이용하여 유출모형을 개발하고 모형의 적합성을 나타내고자 하였다. 이를 위해 분류문제에만 사용되었던 CNN을 활용하였는데, 본 모형의 입력자료로 일반적으로 CNN에서 사용하는 사진을 이용할 수 없으며, 연구의 특성상 유역조건 및 강우 등의 영향이 반영된 수치적(numerical) 이미지(image)를 사용해야 하는 난해점이 있다. 이를 해결하고자 NRCS의 CN을 사용하여 이미지를 생성했으며, CNN 모형의 입력자료로 충분히 활용 가능함을 나타냈다. 이에 더하여, 유출 추정을 위해서만 사용되어왔던 CN의 새로운 용도를 제시할 수 있었다. 모형의 학습 및 검정 결과, 전반적으로 안정적으로 모형의 학습 및 일반화가 이루어졌으며, 관측값과 산정값간의 관계를 나타내는 R2는 0.79로 비교적 높은 값이 나타났다. 또한, 모형의 평가결과는 Pearson 상관계수, NSE, 및 RMSE 등이 각각 0.84, 0.65 및 24.54 m3/s으로 나타나, 전반적으로 양호한 모형의 산정성능을 보인것으로 나타났다.

키워드
합성곱신경망
토양피복형수
딥러닝
유출모형
선형함수
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Information
  • Publisher :Korean Society of Ecology and Infrastructure Engineering
  • Publisher(Ko) :응용생태공학회
  • Journal Title :Ecology and Resilient Infrastructure
  • Journal Title(Ko) :응용생태공학회 논문집
  • Volume : 7
  • No :2
  • Pages :114-125
  • Received Date : 2020-05-12
  • Revised Date : 2020-06-07
  • Accepted Date : 2020-06-10
  • DOI :https://doi.org/10.17820/eri.2020.7.2.114
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