Water level prediction in Taehwa River basin using deep learning model based on DNN and LSTM

Myungjin Lee; Jongsung Kim; Younghoon Yoo; Hung Soo Kim; Sam Eun Kim; Soojun Kim

doi:10.3741/JKWRA.2021.54.S-1.1061

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2021 Vol.54, Issue 12S Preview Page Next Page

Research Article

Water level prediction in Taehwa River basin using deep learning model based on DNN and LSTM DNN 및 LSTM 기반 딥러닝 모형을 활용한 태화강 유역의 수위 예측

31 December 2021. pp. 1061-1069

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Abstract

Recently, the magnitude and frequency of extreme heavy rains and localized heavy rains have increased due to abnormal climate, which caused increased flood damage in river basin. As a result, the nonlinearity of the hydrological system of rivers or basins is increasing, and there is a limitation in that the lead time is insufficient to predict the water level using the existing physical-based hydrological model. This study predicted the water level at Ulsan (Taehwagyo) with a lead time of 0, 1, 2, 3, 6, 12 hours by applying deep learning techniques based on Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) and evaluated the prediction accuracy. As a result, DNN model using the sliding window concept showed the highest accuracy with a correlation coefficient of 0.97 and RMSE of 0.82 m. If deep learning-based water level prediction using a DNN model is performed in the future, high prediction accuracy and sufficient lead time can be secured than water level prediction using existing physical-based hydrological models.

Keywords

Deep learning

Deep neural network

Long short-term memory

Water level prediction

최근 이상 기후로 인해 극한 호우 및 국지성 호우의 규모 및 빈도가 증가하여 하천 주변의 홍수 피해가 증가하고 있다. 이에 따라 하천 또는 유역 내 수문학적 시스템의 비선형성이 증가하고 있으며, 기존의 물리적 기반의 수문 모형을 활용하여 홍수위를 예측하기에는 선행시간이 부족한 한계점이 존재한다. 본 연구에서는 Deep Neural Network (DNN) 및 Long Short-Term Memory (LSTM)기반의 딥러닝 기법을 적용하여 울산시(태화교) 지점의 수위를 0, 1, 2, 3, 6, 12시간에 대해 선행 예측을 수행하였고 예측 정확도를 비교 분석하였다. 그 결과 sliding window 개념을 적용한 DNN 모형이 선행시간 12시간까지 상관계수 0.97, RMSE 0.82 m로 가장 높은 정확도를 보이고 있음을 확인하였다. 향후 DNN 모형을 활용하여 딥러닝 기반의 수위 예측을 수행한다면 기존의 물리적 모형을 통한 홍수위 예측보다 향상된 예측 정확도와 충분한 선행시간을 확보할 수 있을 것으로 판단된다.

키워드

딥러닝

DNN

LSTM

수위 예측

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Information

Publisher :KOREA WATER RESOURECES ASSOCIATION
Publisher(Ko) :한국수자원학회
Journal Title :Journal of Korea Water Resources Association
Journal Title(Ko) :한국수자원학회 논문집
Volume : 54
No :12
Pages :1061-1069
Received Date : 2021-09-27
Revised Date : 2021-10-25
Accepted Date : 2021-10-26
DOI :https://doi.org/10.3741/JKWRA.2021.54.S-1.1061

Journal of Korea Water Resources Association ISSN:2799-8746(Print) 2799-8754(Online) 한국수자원학회 논문집

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