Journal of information and communication convergence engineering

  • 제22권2호
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  • Pages.133-138
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  • 2024
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  • 2234-8255(pISSN)
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  • 2234-8883(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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Prediction of Dissolved Oxygen at Anyang-stream using XG-Boost and Artificial Neural Networks

  • Keun Young Lee (Independent scholar) ;
  • Bomchul Kim (Department of Environmental Science, Kangwon National University) ;
  • Gwanghyun Jo (Department of Mathematical Data Analysis, Hanyang University ERICA)
  • 투고 : 2024.01.01
  • 심사 : 2024.03.04
  • 발행 : 2024.06.30
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초록

Dissolved oxygen (DO) is an important factor in ecosystems. However, the analysis of DO is frequently rather complicated because of the nonlinear phenomenon of the river system. Therefore, a convenient model-free algorithm for DO variable is required. In this study, a data-driven algorithm for predicting DO was developed by combining XGBoost and an artificial neural network (ANN), called ANN-XGB. To train the model, two years of ecosystem data were collected in Anyang, Seoul using the Troll 9500 model. One advantage of the proposed algorithm is its ability to capture abrupt changes in climate-related features that arise from sudden events. Moreover, our algorithm can provide a feature importance analysis owing to the use of XGBoost. The results obtained using the ANN-XGB algorithm were compared with those obtained using the ANN algorithm in the Results Section. The predictions made by ANN-XGB were mostly in closer agreement with the measured DO values in the river than those made by the ANN.

키워드

과제정보

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. 2020R1C1C1A01005396).

참고문헌

  1. K. P. Singh, A. Basant, A. Malik, and G. Jain, "Artificial neural network modeling of the river water quality-a case study," Ecological Modelling, vol. 220, no. 6, pp. 888-895, Mar. 2009. DOI: 10.1016/j.ecolmodel.2009.01.004. 
  2. H. J. Wu, Z. Y. Lin, and S. L. Gao, "The application of artificial neural networks in the resources and environment," Resources and Environment in the Yangtze Basin, vol. 9, no. 2, pp. 241-246, 2000. DOI: 10.1007/s11434-010-4183-3. 
  3. S. Xiang, Z. Liu, and L. Ma, "Study of multivariate linear regression analysis model for ground water quality prediction," Environmental Science, vol. 24, no. 1, pp. 60-62, 2006. 
  4. M. I. Hejazi, X. Cai, and B. L. Ruddell, "The role of hydrologic information in reservoir operation--learning from historical releases," Advances in water resources, vol. 31, no. 12, pp. 1636-1650, Dec. 2008. DOI: 10.1016/j.advwatres.2008.07.013. 
  5. J. Y. Lee, K. Y. Lee, S. Lee, J. Choi, S. J. Lee, S. Jung, M. S. Jung, and B. Kim, "Recovery of fish community and water quality in streams where fish kills have occurred," Korean Journal of Ecology and Environment, vol. 46 no. 2, pp. 154-165, Jun. 2013. DOI: 10.11614/KSL.2013.46.2.154. 
  6. J. E. Nash, and J. V. Sutcliffe, "River flow forecasting through conceptual models part i-a discussion of principles," Journal of hydrology, vol. 10, pp. 282-290, Apr. 1970. DOI: 10.1016/0022-1694(70)90255-6. 
  7. M. A. Pena, S. Katsev, T. Oguz, and D. Gilbert, "Modeling dissolved oxygen dynamics and hypoxia," Biogeosciences, vol. 7, no. 3, pp. 933-957. Mar. 2010. DOI: 10.5194/bg-7-933-2010. 
  8. C.-Y. Liaw, N. Islam, K. K. Phoon, S. Y. Liong, "Comment on does the river run wild? assessing chaos in hydrological systems," Advances in water resources, vol. 24, no. 5, pp. 575-580, 2001. DOI: 10.1016/S0309-1708(00)00053-1. 
  9. V. Z. Antonopoulos and S. K. Gianniou, "Simulation of water temperature and dissolved oxygen distribution in lake vegoritis, Greece," Ecological Modelling, vol. 160, no. 1-2, pp. 39-53, Feb. 2003. DOI: 10.1016/S0304-3800(02)00286-7. 
  10. S. Lek, and J. F. Guegan, "Artificial neural networks as a tool in ecological modelling, an introduction," Ecological modelling, vol. 120, no. 2-3, pp. 65-73, Aug. 1999. DOI: 10.1016/S0304-3800(99)00092-7. 
  11. J. Bowers, and C. Shedrow, "Predicting stream water quality using artificial neural networks (ANN)," WIT Transactions on Ecology and the Environment, vol. 41, 2000. DOI: 10.2495/ENV000081. 
  12. Y. M. Kuo, C. W. Liu, and K. H. Lin, "Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan," Water research, vol. 38, no. 1, pp. 148-158, Jan. 2004. DOI: 10.1016/j.watres.2003.09.026. 
  13. G. Sahoo, S. Schladow, and J. Reuter, "Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models," Journal of hydrology, vol. 378, no. 3-4, pp. 25-342, Nov. 2009. DOI: j.jhydrol.2009.09.037. 
  14. S. Palani, S. Y. Liong, and P. Tkalich, "An ANN application for water quality forecasting," Marine pollution bulletin, vol. 56, no. 9, pp. 15861597, Sep. 2008. DOI: 10.1016/j.marpolbul.2008.05.021. 
  15. V. Nourani, M. Komasi, and A. Mano, "A multivariate ANN-wavelet approach for rainfall--runoff modeling," Water resources management, vol. 23, pp. 2877-2894, Feb. 2009. DOI: 10.1007/s11269-009-9414-5. 
  16. A. Kavousi-Fard, "A new fuzzy-based feature selection and hybrid TLA-ANN modelling for short-term load forecasting," Journal of Experimental & Theoretical Artificial Intelligence, vol. 25, no. 4, pp. 543-557. May 2013. DOI: 10.1080/0952813X.2013.782350. 
  17. M. K. Jha, and S. Sahoo, "Efficacy of neural network and genetic algorithm techniques in simulating spatio-temporal fluctuations of groundwater," Hydrological processes, vol. 29, no. 5, pp. 671-691, Feb. 2015. DOI: 10.1002/hyp.10166. 
  18. M. Ravansalar, T. Rajaee, and M. Ergil, "Prediction of dissolved oxygen in river calder by noise elimination time series using wavelet transform," Journal of Experimental & Theoretical Artificial Intelligence, vol. 28, no. 4, pp. 689-706, May 2015.DOI: 10.1080/0952813X.2015.1042531. 
  19. T. Chen, and C. Guestrin, "Xgboost: A scalable tree boosting system," Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, ACM, pp. 785- 794, Aug. 2016. DOI: 10.1145/2939672.2939785. 
  20. J. H. Friedman, "Greedy function approximation: a gradient boosting machine," The Annals of statistics, vol. 29, no. 5, pp. 1189-1232, Oct. 2001. 
  21. M. Gumus, and M. S. Kiran, "Crude oil price forecasting using XGboost," in 2017 International Conference on Computer Science and Engineering (UBMK), Antalya, TR, pp. 1100-1103, 2017. DOI: 10.1109/UBMK.2017.8093500. 
  22. L. Zhang, and C. Zhan, "Machine learning in rock facies classification: an application of XGboost," in International Geophysical Conference, Qingdao, CN, pp. 1371-1374, 2017. DOI: 10.1190/IGC2017-351. 
  23. Z. Chen, F. Jiang, Y. Cheng, X. Gu, W. Liu, and J. Peng, "Xgboost classifier for DDoS attack detection and analysis in SDN-based cloud," in 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), Shanghai, CN, pp. 251-256, 2018. DOI: 10.1109/BigComp.2018.00044. 
  24. H. Zheng, J. Yuan, and L. Chen, "Short-term load forecasting using EMD-LSTM neural networks with a XGboost algorithm for feature importance evaluation," Energies, vol. 10, no. 18, Aug. 2017. DOI: 10.3390/en10081168.