Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Design of Artificial Intelligence Water Level Prediction System for Prediction of River Flood

하천 범람 예측을 위한 인공지능 수위 예측 시스템 설계

  • Park, Se-Hyun (Department of Electronic Engineering, Andong National University) ;
  • Kim, Hyun-Jae (Major in Electronic Engineering, Department of Information and Communication, Andong National University)
  • Received : 2019.12.17
  • Accepted : 2019.12.26
  • Published : 2020.02.29
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Abstract

In this paper, we propose an artificial water level prediction system for small river flood prediction. River level prediction can be a measure to reduce flood damage. However, it is difficult to build a flood model in river because of the inherent nature of the river or rainfall that affects river flooding. In general, the downstream water level is affected by the water level at adjacent upstream. Therefore, in this study, we constructed an artificial intelligence model using Recurrent Neural Network(LSTM) that predicts the water level of downstream with the water level of two upstream points. The proposed artificial intelligence system designed a water level meter and built a server using Nodejs. The proposed neural network hardware system can predict the water level every 6 hours in the real river.

본 논문에서는 소규모 강의 범람 예측을 위한 인공 수위 예측 시스템을 제안한다. 강의 수위 예측은 홍수 피해를 줄일 수 있는 대책이 될 수 있다. 그러나 하천 범람에 영향을 미치는 강 또는 강우의 고유 특성으로 인해 범람 모델을 구축하기가 어렵다. 일반적으로 하류 수위는 상류의 인접한 수위에 영향을 받는다. 따라서 본 연구에서는 측정 지점에서 수위를 예측하기 위해 두 개의 상류 측정 지점의 수위를 순환신경망(LSTM)을 사용하여 인공 지능 모델을 구축했다. 제안 된 인공 지능 시스템은 수위 측정기를 설계하고 Nodejs를 사용하여 서버를 구축했다. 제안 된 신경망 하드웨어 시스템은 실제 강에서 6시간마다 수위를 잘 예측함을 알 수 있었다.

Keywords

References

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