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

Korea Institute of Electronic Communication Science (한국전자통신학회)
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Groundwater Level Prediction using ANFIS Algorithm

딥러닝을 이용한 하천 유량 예측 알고리즘

  • 박귀만 (전남대학교 전기 및 반도체공학과) ;
  • 오세랑 (전남대학교 전기 및 반도체공학과) ;
  • 박근호 (전남대학교 전기 및 반도체공학과) ;
  • 배영철 (전남대학교 전기.전자통신.컴퓨터공학부)
  • Received : 2021.09.05
  • Accepted : 2021.12.17
  • Published : 2021.12.31
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Abstract

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.

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.

Keywords

Acknowledgement

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

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