Journal of The Korean Society of Agricultural Engineers (한국농공학회논문집)

The Korean Society of Agricultural Engineers (한국농공학회)
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Evaluation of Surrogate Monitoring Parameters for SS and T-P Using Multiple Linear Regression and Random Forest

다중 선형 회귀 분석과 랜덤 포레스트를 이용한 SS, T-P 대리모니터링 기법 평가

  • Jeung, Minhyuk (Department of Rural and Bio-Systems Engineering, Chonnam National University) ;
  • Beom, Jina (Department of Rural and Bio-Systems Engineering, Chonnam National University) ;
  • Choi, Dongho (Presidential Water Commission Support Department Planning and Operation, Republic of Korea Presidential Water Commission) ;
  • Kim, Young-joo (Department of Cadastre and Civil Engineering, VISION College of Jeonju) ;
  • Her, Younggu (Tropical Research and Education, Department of Agricultural and Biological Engineering, University of Florida) ;
  • Yoon, Kwangsik (Department of Rural and Bio-Systems Engineering, Chonnam National University)
  • Received : 2020.11.27
  • Accepted : 2021.02.03
  • Published : 2021.03.31
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Abstract

Effective nonpoint source (NPS) pollution management requires frequent water quality monitoring, which is, however, often costly to be implemented in practice. Statistical techniques and machine learning methods allow us to identify and focus on fundamental environmental variables that have close relationships with NPS pollutants of interest. This study developed surrogate models to predict the concentrations of suspended sediment (SS) and total phosphorus (T-P) from turbidity and runoff discharge rates using multiple linear regression (MLR) and random forest (RF) methods. The RF models provided acceptable performance in predicting SS and T-P, especially when runoff discharge rates were high. The RF models outperformed the MLR models in all the cases. Such finding highlights the potential of RF techniques and models as a tool to identify fundamental environmental variables that are measured in relatively inexpensive ways or freely available but still able to provide information required to quantify the concentrations of NP S pollutants. The analysis of relative importance rates showed that the temporal variations of SS and T-P concentrations could be more effectively explained by that of turbidity than runoff discharge rate. This study demonstrated that the advanced statistical techniques such as machine learning could help to improve the efficiency of NPS pollutants monitoring.

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References

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