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2021 Vol.54, Issue 12S Preview Page

Research Article

Novel two-stage hybrid paradigm combining data pre-processing approaches to predict biochemical oxygen demand concentration

생물화학적 산소요구량 농도예측을 위하여 데이터 전처리 접근법을 결합한 새로운 이단계 하이브리드 패러다임

Sungwon Kima*
,
Youngmin Seob
,
Mousaab Zakhroufc
,
Anurag Malikd
김 성원a*
,
서 영민b
,
자크로프 마샵c
,
말릭 아누락d
aFull Professor, Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, Korea
bLecturer, Department of Constructional and Environmental Engineering, Kyungpook National University, Sangju, Korea
cResearch Associate, URMER Laboratory, Department of Hydraulics, Faculty of Technology, University of Tlemcen, Tlemcen, Algeria
dScientist, Punjab Agricultural University, Regional Research Station, Bathinda, Punjab, India
a동양대학교 철도건설안전공학과 정교수
b경북대학교 건설환경공학과 강사
c알제리 트렘슨대학교 수리학과 연구원
d인도 펀잡 농업대학교 지역연구단 과학자
*Corresponding Author
31 December 2021. pp. 1037-1051
PDF XML Citation
Abstract

Biochemical oxygen demand (BOD) concentration, one of important water quality indicators, is treated as the measuring item for the ecological chapter in lakes and rivers. This investigation employed novel two-stage hybrid paradigm (i.e., wavelet-based gated recurrent unit, wavelet-based generalized regression neural networks, and wavelet-based random forests) to predict BOD concentration in the Dosan and Hwangji stations, South Korea. These models were assessed with the corresponding independent models (i.e., gated recurrent unit, generalized regression neural networks, and random forests). Diverse water quality and quantity indicators were implemented for developing independent and two-stage hybrid models based on several input combinations (i.e., Divisions 1-5). The addressed models were evaluated using three statistical indices including the root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (CC). It can be found from results that the two-stage hybrid models cannot always enhance the predictive precision of independent models confidently. Results showed that the DWT-RF5 (RMSE = 0.108 mg/L) model provided more accurate prediction of BOD concentration compared to other optimal models in Dosan station, and the DWT-GRNN4 (RMSE = 0.132 mg/L) model was the best for predicting BOD concentration in Hwangji station, South Korea.

Keywords
Biochemical oxygen demand
Gated recurrent unit
Generalized regression neural networks
Random forests
Discrete wavelet transform
Water quality indicator

주요한 수질지표 중의 하나인 생물화학적 산소요구량(BOD) 농도는 호소와 하천에서 생태학적 측면에서 관측항목으로 취급하고 있다. 본 연구에서는 대한민국의 도산 및 황지지점에서 BOD 농도예측을 위하여 새로운 이단계 하이브리드 패러다임(웨이블릿 기반 게이트 순환 유닛, 웨이블릿 기반 일반화된 회귀신경망, 그리고 웨이블릿 기반 랜덤 포레스트) 을 활용하였다. 이러한 모형들은 각 대응하는 독립모형들(게이트 순환 유닛, 일반화된 회귀신경망, 그리고 랜덤 포레스트) 과 함께 평가되었다. 다양한 수질 및 수량지표들이 여러 개의 입력조합(분류1-5) 을 기본으로 하여 독립 및 이단계 하이브리드 모형을 개발하기 위하여 구현되었다. 언급한 모형들은 root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), 그리고 correlation coefficient (CC) 를 포함한 세 개의 통계지표로서 평가되었으며, 통계결과치를 분석하면 이단계 하이브리드 모형들이 항상 대응하는 독립모형들의 예측 정도를 개선하지 않은 것으로 나타났다. 대한민국의 도산관측소에서는 DWT-RF5 (RMSE = 0.108 mg/L) 모형이 다른 최적모형과 비교하여 BOD 농도의 더 정확한 예측을 나타내었으며, 황지관측소에서는 DWT-GRNN4 (RMSE = 0.132 mg/L) 모형이 BOD 농도를 예측하는 최고의 모형이다.

키워드
생물화학적 산소요구량
게이트 순환 유닛
일반화된 회귀신경망
랜덤 포레스트
이산 웨이블릿 변환
수질지표
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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 :1037-1051
  • Received Date : 2021-08-18
  • Revised Date : 2021-09-26
  • Accepted Date : 2021-10-06
  • DOI :https://doi.org/10.3741/JKWRA.2021.54.S-1.1037
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