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

김성원; 서영민; 자크로프 마샵; 말릭 아누락

doi:10.3741/jkwra.2021.54.s-1.1037

생물화학적 산소요구량 농도예측을 위하여 데이터 전처리 접근법을 결합한 새로운 이단계 하이브리드 패러다임
Novel two-stage hybrid paradigm combining data pre-processing approaches to predict biochemical oxygen demand concentration 원문보기

Journal of Korea Water Resources Association = 한국수자원학회논문집, v.54 no.12 suppl., 2021년, pp.1037 - 1051

김성원 (동양대학교 철도건설안전공학과) , 서영민 (경북대학교 건설환경공학과) , 자크로프 마샵 (알제리 트렘슨대학교 수리학과) , 말릭 아누락 (인도 펀잡 농업대학교 지역연구단)

초록
AI-Helper

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.

Abstract ▼ AI-Helper

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.

주제어

표/그림 (17)

그림 Fig. 1. Flowchart of investigation steps
그림 Fig. 2. A schematic diagram of gated recurrent unit (GRU) model
그림 Fig. 3. A schematic diagram of generalized regression neural networks (GRNN) model
그림 Fig. 4. A schematic diagram of random forests (RF) model (Alizamir et al., 2021)
그림 Fig. 5. Two-level DWT approach employing Mallat algorithm (Kim et al., 2017)
그림 Fig. 6. Flowchart for developing two-stage hybrid models
그림 Fig. 7. The schematic maps of Dosan and Hwangji stations
표 Table 1. Statistical properties of water quality and quantity indicators
표 Table 2. Computation of correlations between corresponding input indicator and BOD concentration
표 Table 3. Diverse input combinations of independent and two-stage hybrid models
그림 Fig. 8. Comparison of observed and predicted BOD concentration values for the optimal independent models during testing step (Dosan station)
표 Table 4. RMSE, NSE, and CC values for the independent and two-stage hybrid models at Dosan station
그림 Fig. 9. Comparison of observed and predicted BOD concentration values for the optimum two-stage hybrid models during testing step (Dosan station)
그림 Fig. 10. Comparison of observed and predicted BOD concentration values for the optimal independent models during testing step (Hwangji station)
표 Table 5. RMSE, NSE, and CC values for the independent and two-stage hybrid models at Hwangji station
그림 Fig. 11. Comparison of observed and predicted BOD concentration values for the optimum two-stage hybrid models during testing step (Hwangji station)
그림 Fig. 12. Taylor schemes based on the optimal independent and two-stage hybrid models at both station

참고문헌 (59)

Ahmadi, A., Fatemi, Z., and Nazari, S. (2018). "Assessment of input data selection methods for BOD simulation using data-driven models: A case study." Environmental Monitoring and Assessment, Vol. 190, No. 4, p. 239.

상세보기
Ahmadi, A., Nasseri, M., and Solomatine, D.P. (2019). "Parametric uncertainty assessment of hydrological models: coupling UNEEC-P and a fuzzy general regression neural network." Hydrological Sciences Journal, Vol. 64, No. 9, pp. 1080-1094.

상세보기
Ahmed, A.A.M., and Shah, S.M.A. (2017). "Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River." Journal of King Saud University-Engineering Sciences, Vol. 29, No. 3, pp. 237-243.

상세보기
Alizamir, M., Kim, S., Zounemat-Kermani, M., Heddam, S., Shahrabadi, A.H., and Gharabaghi, B. (2021). "Modelling daily soil temperature by hydro-meteorological data at different depths using a novel data-intelligence model: Deep echo state network model." Artificial Intelligence Review, Vol. 54, No. 4, pp. 2863-2890.

상세보기
Ay, M., and Kisi, O. (2012). "Modeling of dissolved oxygen concentration using different neural network techniques in Foundation Creek, El Paso County, Colorado." Journal of Environmental Engineering, Vol. 138, No. 6, pp. 654-662.

상세보기
Breiman, L. (2001). "Random forests." Machine Learning, Vol. 45, No. 1, pp. 5-32.
Cho, K., Van Merrienboer, B., Bahdanau, and D., Bengio, Y. (2014). "On the properties of neural machine translation: Encoder-decoder approaches." arXiv preprint arXiv, 1409. 1259. doi: 10.3115/v1/W14-4012
Deo, R.C., Sahin, M., Adamowski, J.F., and Mi, J. (2019). "Universally deployable extreme learning machines integrated with remotely sensed MODIS satellite predictors over Australia to forecast global solar radiation: A new approach." Renewable and Sustainable Energy Reviews, Vol. 104, pp. 235-261.

상세보기
Diamantopoulou, M.J., Antonopoulos, V.Z., and Papamichail, D.M. (2007). "Cascade correlation artificial neural networks for estimating missing monthly values of water quality parameters in rivers." Water Resources Management, Vol. 21, No. 3, pp. 649-662.

상세보기
Dogan, E., Sengorur, B., and Koklu, R. (2009). "Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique." Journal of Environmental Management, Vol. 90, Issue 2, pp. 1229-1235.

상세보기
Emamgholizadeh, S., Kashi, H., Marofpoor, I., and Zalaghi, E. (2014). "Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models." International Journal of Environmental Science and Technology, Vol. 11, No. 3, pp. 645-656.

상세보기
Fallah, H., Kisi, O., Kim, S., and Rezaie-Balf, M. (2019). "A new optimization approach for the least-cost design of water distribution networks: Improved crow search algorithm." Water Resources Management, Vol. 33, No. 10, pp. 3595-3613.

상세보기
Friedman, J.H. (2002). "Stochastic gradient boosting." Computational Statistics and Data Analysis, Vol. 38, No. 4, pp. 367-378.

상세보기
Garrick, M., Cunnane, C., and Nash, J.E. (1978). "A criterion of efficiency for rainfall-runoff models." Journal of Hydrology, Vol. 36, No. 3-4, pp. 375-381.

상세보기
Granata, F., Papirio, S., Esposito, G., Gargano, R., and de Marinis, G. (2017). "Machine learning algorithms for the forecasting of wastewater quality indicators." Water, Vol. 9, No. 2, p. 105.

상세보기
Jouanneau, S., Recoules, L., Durand, M.J., Boukabache, A., Picot, V., Primault, Y., Lakel, A., Sengelin, M., Barillon, B., and Thouand, G. (2014). "Methods for assessing biochemical oxygen demand (BOD): A review." Water Research, Vol. 49, pp. 62-82.

상세보기
Kalteh, A.M. (2015). "Wavelet genetic algorithm-support vector regression (wavelet GA-SVR) for monthly flow forecasting." Water Resources Management, Vol. 29, No. 4, pp.1283-1293.

상세보기
Khaled, B., Abdellah, A., Noureddine, D., Salim, H., and Sabeha, A. (2017). "Modelling of biochemical oxygen demand from limited water quality variable by ANFIS using two partition methods." Water Quality Research Journal of Canada, Vol. 53, No. 1, pp. 24-40.
Kim, S. (2000). "The application of neural networks method for the flood discharge forecasting in the river basin." Journal of Korean Society of Civil Engineers, Vol. 20, No. 6-B, pp. 801-811 (in Korean).
Kim, S. (2011). "Nonlinear hydrologic modeling using the stochastic and neural networks approach." Disaster Advances, Vol. 4, No. 1, pp. 53-63.

상세보기
Kim, S., Alizamir, M., Zounemat-Kermani, M., Kisi, O., and Singh, V.P. (2020). "Assessing the biochemical oxygen demand using neural networks and ensemble tree approaches in South Korea." Journal of Environmental Management, Vol. 270, p. 110834.

상세보기
Kim, S., and Kim, H.S. (2007). "Neural networks-genetic algorithm model for modeling of nonlinear evaporation and evapotranspiration time series 1. Theory and application of the model." Journal of Korean Water Resources Association, Vol. 40, No. 1, pp. 73-88. (in Korean)

원문보기 상세보기
Kim, S., and Kim, H.S. (2008). "Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling." Journal of Hydrology, Vol. 351, No. 3-4, pp. 299-317.

상세보기
Kim, S., Kim, J.H., and Park, K.B. (2009). "Statistical learning theory for the disaggregation of the climatic data." Proceedings of the 33rd IAHR Congress, Vancouver, Canada, pp. 1154-1162.
Kim, S., Kisi, O., Seo, Y., Singh, V.P., and Lee, C.J. (2017). "Assessment of rainfall aggregation and disaggregation using data-driven models and wavelet decomposition." Hydrology Research, Vol. 48, No. 1, pp. 99-116.

상세보기
Kim, S., Maleki, N., Rezaie-Balf, M., Singh, V.P., Alizamir, M., Kim, N.W., Lee, J.T., and Kisi, O. (2021). "Assessment of the total organic carbon employing the different nature-inspired approaches in the Nakdong River, South Korea." Environmental Monitoring and Assessment, Vol. 193, No. 7, pp.1-22.

상세보기
Kim, S., Park, K.B., and Seo, Y.M. (2012). "Estimation of pan evaporation using neural networks and climate-based models." Disaster Advances, Vol. 5, No. 3, pp. 34-43.

상세보기
Kim, S., Seo, Y., and Lee, C.J. (2016). "Modeling of rainfall by combining neural computation and wavelet technique." Procedia Engineering, Vol. 154, pp. 1231-1236.

상세보기
Kisi, O. (2006). "Generalized regression neural networks for evapotranspiration modelling." Hydrological Sciences Journal, Vol. 51, No. 6, pp. 1092-1105.

상세보기
Ladlani, I., Houichi, L., Djemili, L., Heddam, S., and Belouz, K. (2012). "Modeling daily reference evapotranspiration (ET o ) in the north of Algeria using generalized regression neural networks (GRNN) and radial basis function neural networks (RBFNN): A comparative study." Meteorology and Atmospheric Physics, Vol. 118, No. 3, pp. 163-178.

상세보기
Li, J., Abdulmohsin, H.A., Hasan, S.S., Kaiming, L., Al-Khateeb, B., Ghareb, M.I., and Mohammed, M.N. (2019). "Hybrid soft computing approach for determining water quality indicator: Euphrates River." Neural Computing and Applications, Vol. 31, No. 3, pp. 827-837.

상세보기
Li, X., Zecchin, A.C., and Maier, H.R. (2014). "Selection of smoothing parameter estimators for general regression neural networks - applications to hydrological and water resources modelling." Environmental Modelling and Software, Vol. 59, pp. 162-186.

상세보기
Mallat, S.G. (1989). "A theory of multiresolution signal decomposition: the wavelet representation." IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, No. 3, pp. 674-693.

상세보기
Ministry of Environment (ME) (2020). Full-scale implementation of the total water pollution control system in the 2030 phase of the Four Major Rivers (7.15). Press release.
Nash, J.E., and Sutcliffe, J.V. (1970). "River flow forecasting through conceptual models, Part 1 - A discussion of principles." Journal of Hydrology, Vol. 10, No. 3, pp. 282-290.

상세보기
Noori, R., Yeh, H.D., Abbasi, M., Kachoosangi, F.T., and Moazami, S. (2015). "Uncertainty analysis of support vector machine for online prediction of five-day biochemical oxygen demand." Journal of Hydrology, Vol. 527, pp. 833-843.

상세보기
Percival, D.B., and Walden, A.T. (2000). Wavelet methods for time series analysis. Cambridge University Press, New York, NY, U.S.
Raheli, B., Aalami, M.T., El-Shafie, A., Ghorbani, M.A., and Deo, R.C. (2017). "Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: A case study of Langat River." Environmental Earth Sciences, Vol. 76, No. 14, p. 503.

상세보기
Rezaie-Balf, M., Maleki, N., Kim, S., Ashrafian, A., Babaie-Miri, F., Kim, N.W., Chung, I.M., and Alaghmand, S. (2019). "Forecasting daily solar radiation using CEEMDAN decomposition-based MARS model trained by crow search algorithm." Energies, Vol. 12, No. 8, p. 1416.

상세보기
Royal Commission on Sewage Disposal (1908). Fifth report on methods of treating and disposing of sewage. UK.
Sahay, R.R., and Srivastava, A. (2014). "Predicting monsoon floods in rivers embedding wavelet transform, genetic algorithm and neural network." Water Resources Management, Vol. 28, No. 2, pp. 301-317.

상세보기
Seo, Y., and Kim, S. (2016). "Hydrological forecasting using hybrid data-driven approach." American Journal of Applied Sciences, Vol. 13, No. 8, pp.891-899.

상세보기
Seo, Y., Kim, S., and Singh, V.P. (2018). "Comparison of different heuristic and decomposition techniques for river stage modeling." Environmental Monitoring and Assessment, Vol. 190, No. 7, pp. 1-22.

상세보기
Seo, Y., Kim, S., Kisi, O., and Singh, V.P. (2015). "Daily water level forecasting using wavelet decomposition and artificial intelligence techniques." Journal of Hydrology, Vol. 520, pp. 224-243.

상세보기
Seo, Y., Kim, S., Kisi, O., Singh, V.P., and Parasuraman, K. (2016). "River stage forecasting using wavelet packet decomposition and machine learning models." Water Resources Management, Vol. 30, No. 11, pp. 4011-4035.

상세보기
Simard, M., Saatchi, S.S., and De Grandi, G. (2000). "The use of decision tree and multiscale texture for classification of JERS-1 SAR data over tropical forest." IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 5, pp. 2310-2321.

상세보기
Solgi, A., Pourhaghi, A., Bahmani, R., and Zarei, H. (2017). "Improving SVR and ANFIS performance using wavelet transform and PCA algorithm for modeling and predicting biochemical oxygen demand (BOD)." Ecohydrology and Hydrobiology, Vol. 17, No. 2, pp.164-175.

상세보기
Specht, D.F. (1991). "A general regression neural network." IEEE Transactions on Neural Networks, Vol. 2, No. 6, pp. 568-576.

상세보기
Tao, H., Bobaker, A.M., Ramal, M.M., Yaseen, Z.M., Hossain, M.S., and Shahid, S. (2019). "Determination of biochemical oxygen demand and dissolved oxygen for semi-arid river environment: application of soft computing models." Environmental Science and Pollution Research, Vol. 26, No. 1, pp. 923-937.

상세보기
Taylor, K.E. (2001). "Summarizing multiple aspects of model performance in a single diagram." Journal of Geophysical Research: Atmospheres, Vol. 106, No. D7, pp. 7183-7192.

상세보기
Willmott, C.J., and Matsuura, K. (2005). "Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance." Climate Research, Vol. 30, No. 1, pp. 79-82.

상세보기
Yang, G., Lee, H., and Lee, G. (2020). "A hybrid deep learning model to forecast particulate matter concentration levels in Seoul, South Korea." Atmosphere, Vol. 11, No. 4, p. 348.

상세보기
Yaseen, Z.M., Karami, H., Ehteram, M., Mohd, N.S., Mousavi, S.F., Hin, L.S., Kisi, O., Farzin, S., Kim, S., and El-Shafie, A. (2018). "Optimization of reservoir operation using new hybrid algorithm." KSCE Journal of Civil Engineering, Vol. 22, No. 11, pp. 4668-4680.

상세보기
Zakhrouf, M., Bouchelkia, H., Stamboul, M., and Kim, S. (2020). "Novel hybrid approaches based on evolutionary strategy for streamflow forecasting in the Chellif River, Algeria." Acta Geophysica, Vol. 68, No. 1, pp.167-180.

상세보기
Zakhrouf, M., Bouchelkia, H., Stamboul, M., Kim, S., and Heddam, S. (2018). "Time series forecasting of river flow using an integrated approach of wavelet multi-resolution analysis and evolutionary data-driven models. A case study: Sebaou River (Algeria)." Physical Geography, Vol. 39, No. 6, pp. 506-522.

상세보기
Zhang, Y., Pulliainen, J., Koponen, S., and Hallikainen, M. (2002). "Application of an empirical neural network to surface water quality estimation in the Gulf of Finland using combined optical data and microwave data." Remote Sensing of Environment, Vol. 81, No. 2-3, pp. 327-336.

상세보기
Zou, R., Lung, W.S., and Wu, J. (2007). "An adaptive neural network embedded genetic algorithm approach for inverse water quality modeling." Water Resources Research, Vol. 43, No. 8, W08427.

상세보기
Zounemat-Kermani, M., Rajaee, T., Ramezani-Charmahineh, A., and Adamowski, J.F. (2017). "Estimating the aeration coefficient and air demand in bottom outlet conduits of dams using GEP and decision tree methods." Flow Measurement and Instrumentation, Vol. 54, pp. 9-19.

상세보기
Zounemat-Kermani, M., Seo, Y., Kim, S., Ghorbani, M.A., Samadianfard, S., Naghshara, S., Kim, N.W., and Singh, V.P. (2019). "Can decomposition approaches always enhance soft computing models? Predicting the dissolved oxygen concentration in the St. Johns River, Florida." Applied Sciences, Vol. 9, No. 12, p. 2534.

상세보기

저자의 다른 논문 :

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증