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2020 Vol.53, Issue 6

Research Article

Implementation on the evolutionary machine learning approaches for streamflow forecasting: case study in the Seybous River, Algeria

유출예측을 위한 진화적 기계학습 접근법의 구현: 알제리 세이보스 하천의 사례연구

Mousaab Zakhroufa
,
Hamid Bouchelkiab
,
Madani Stamboulc
,
Sungwon Kimd*
,
Vijay P. Singhe
자크로프 마샵a
,
보첼키아 하미드b
,
스탬바울 마대니c
,
김 성원d*
,
싱 비제이e
aURMER Laboratory, Faculty of Technology, Department of Hydraulics, University of Tlemcen
bURMER Laboratory, Faculty of Technology, Department of Hydraulics, University of Tlemcen
cResearch Laboratory of Water Resources, Soil and Environment, Faculty of Architecture and Civil Engineering, Department of Civil Engineering, Amar Telidji University
dProfessor, Department of Railroad Construction and Safety Engineering, Dongyang University
eDistinguished Professor, Regents Professor, and Caroline & William N. Lehrer Distinguished Chair in Water Engineering, Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A&M University
a알제리 트렘슨대학교 수리학과 교원
b알제리 트렘슨대학교 수리학과 교원
c알제리 아마테리지 대학교 토목공학과 교원
d동양대학교 철도건설안전공학과 정교수
e미국 텍사스 A&M 대학교 생물농업공학과 석좌교수
* Corresponding Author
30 June 2020. pp. 395-408
PDF XML Citation
Abstract

This paper aims to develop and apply three different machine learning approaches (i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and wavelet-based neural networks (WNN)) combined with an evolutionary optimization algorithm and the k-fold cross validation for multi-step (days) streamflow forecasting at the catchment located in Algeria, North Africa. The ANN and ANFIS models yielded similar performances, based on four different statistical indices (i.e., root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and peak flow criteria (PFC)) for training and testing phases. The values of RMSE and PFC for the WNN model (e.g., RMSE = 8.590 m3/sec, PFC = 0.252 for (t+1) day, testing phase) were lower than those of ANN (e.g., RMSE = 19.120 m3/sec, PFC = 0.446 for (t+1) day, testing phase) and ANFIS (e.g., RMSE = 18.520 m3/sec, PFC = 0.444 for (t+1) day, testing phase) models, while the values of NSE and R for WNN model were higher than those of ANNs and ANFIS models. Therefore, the new approach can be a robust tool for multi-step (days) streamflow forecasting in the Seybous River, Algeria.

Keywords
Multi-step-days streamflow forecasting
Artificial neural networks
Adaptive neuro-fuzzy inference systems
Wavelet-based neural networks
Genetic algorithm
K-fold cross validation

본 연구논문은 북부아프리카의 알제리에 위치한 하천유역에서 다중선행일 유출량의 예측을 위하여 진화적 최적화기법과 k-fold 교차검증을 결합한 세 개의 서로 다른 기계학습 접근법 (인공신경망, 적응 뉴로퍼지 시스템, 그리고 웨이블릿 기반 신경망)을 개발하고 적용하는 것이다. 인공신경망과 적응 뉴로퍼지 시스템은 root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), 그리고 peak flow criteria (PFC) 의 네 개의 통계지표를 기반으로 하여 모형의 훈련 및 테스팅 결과 유사한 모형수행결과를 나타내었다. 웨이블릿 기반 신경망모형은 하루선행일 테스팅의 결과 RMSE = 8.590 m3/sec 과 PFC = 0.252 로 분석되어서 인공신경망의 RMSE = 19.120 m3/sec, PFC = 0.446 과 적응 뉴로퍼지 시스템의 RMSE = 18.520 m3/sec, PFC = 0.444 보다 양호한 결과를 나타내었고, NSE와 R의 값도 웨이블릿 기반 신경망모형이 우수한 것으로 나타났다. 그러므로 웨이블릿 기반 신경망은 알제리 세이보스 하천에서 다중선행일의 예측을 위하여 효율적인 도구로 사용할 수 있다.

키워드
다중선행일 유출량예측
인공신경망
적응 뉴로퍼지 시스템
웨이블릿 기반 신경망
유전자 알고리즘
k-fold 교차검증
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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 : 53
  • No :6
  • Pages :395-408
  • Received Date : 2020-01-15
  • Revised Date : 2020-03-29
  • Accepted Date : 2020-03-29
  • DOI :https://doi.org/10.3741/JKWRA.2020.53.6.395
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