한국전자통신학회논문지 (The Journal of the Korea institute of electronic communication sciences)

한국전자통신학회 (Korea Institute of Electronic Communication Science)
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딥러닝을 이용한 하천 유량 예측 알고리즘

Groundwater Level Prediction using ANFIS Algorithm

  • 박귀만 (전남대학교 전기 및 반도체공학과) ;
  • 오세랑 (전남대학교 전기 및 반도체공학과) ;
  • 박근호 (전남대학교 전기 및 반도체공학과) ;
  • 배영철 (전남대학교 전기.전자통신.컴퓨터공학부)
  • 투고 : 2021.09.05
  • 심사 : 2021.12.17
  • 발행 : 2021.12.31
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초록

본 논문은 학문적인 이해를 기반을 둔 예측을 수행하기 위해 FDNN(: Flood drought index neural network) 알고리즘을 제시한다. 데이터에 의존한 예측이 아닌 학문적인 이해를 기반을 둔 예측을 딥러닝에 적용하기 위해, 알고리즘을 수리, 수문학을 기반으로 구성하였다. 강수량의 입력으로 하천의 유량을 예측하는 모델을 구성하여 K-교차검증을 통해 모델의 성능을 측정한다. 제시한 알고리즘의 성능을 증명하기 위해 시계열 예측에서 가장 많이 사용되는 LSTM(: Long short term memory) 알고리즘의 예측 성능과 비교하여 제시한 알고리즘의 우수성을 나타낸다.

In this paper, we present FDNN algorithm to perform prediction based on academic understanding. In order to apply prediction based on academic understanding rather than data-dependent prediction to deep learning, we constructed algorithm based on mathematical and hydrology. We construct a model that predicts flow rate of a river as an input of precipitation, and measure the model's performance through K-fold cross validation.

키워드

과제정보

This study was financially supported by Chonn am National University(G-KIRI)

참고문헌

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