Journal of the Korean Society of Marine Environment & Safety (해양환경안전학회지)

The Korean Society of Marine Environment and safety (해양환경안전학회)
권호 표지
DOI QR코드

DOI QR Code

Development of a Water Quality Indicator Prediction Model for the Korean Peninsula Seas using Artificial Intelligence

인공지능 기법을 활용한 한반도 해역의 수질평가지수 예측모델 개발

  • Received : 2023.01.25
  • Accepted : 2023.02.24
  • Published : 2023.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Rapid industrialization and urbanization have led to severe marine pollution. A Water Quality Index (WQI) has been developed to allow the effective management of marine pollution. However, the WQI suffers from problems with loss of information due to the complex calculations involved, changes in standards, calculation errors by practitioners, and statistical errors. Consequently, research on the use of artificial intelligence techniques to predict the marine and coastal WQI is being conducted both locally and internationally. In this study, six techniques (RF, XGBoost, KNN, Ext, SVM, and LR) were studied using marine environmental measurement data (2000-2020) to determine the most appropriate artificial intelligence technique to estimate the WOI of five ecoregions in the Korean seas. Our results show that the random forest method offers the best performance as compared to the other methods studied. The residual analysis of the WQI predicted score and actual score using the random forest method shows that the temporal and spatial prediction performance was exceptional for all ecoregions. In conclusion, the RF model of WQI prediction developed in this study is considered to be applicable to Korean seas with high accuracy.

급격한 산업화와 도시화로 인해 해양 오염이 심각해지고 있으며, 이러한 해양 오염을 실효적으로 관리하기 위해 수질평가지수(Water Quality Index, WQI)를 마련하여 활용하고 있다. 하지만 수질평가지수는 다소 복잡한 계산과정으로 인한 정보의 손실, 기준값 변동, 실무자의 계산오류, 통계적 오류 등의 불확실성(uncertainty)을 내포하고 있다. 이에 따라 국내·외에서 인공지능 기법을 활용하여 수질평가지수를 예측하기 위한 연구가 활발히 이루어지고 있다. 본 연구에서는 해양환경측정망 자료(2000 ~ 2020년)를 활용하여 우리나라 전 해역 즉, 5개의 생태구에 대한 WQI를 추정할 수 있는 가장 적합한 인공지능기법을 도출하기 위해 총 6가지의 기법(RF, XGBoost, KNN, Ext, SVM, LR)을 실험하였다. 그 결과, Random Forest 기법이 다른 기법에 비해 가장 우수한 성능을 보였다. Random Forest 기법의 WQI 점수 예측값과 실제값의 잔차 분석 결과, 모든 생태구에서 시간적 및 공간적 예측 성능이 우수한 것으로 나타났다. 이를 통해 본 연구에서 개발한 Random Forest 기법은 높은 정확도를 바탕으로 우리나라 전해역에 대한 WQI를 예측 가능할 것으로 사료된다.

Keywords

Acknowledgement

이 논문은 2023년도 정부(해양수산부)의 재원으로 해양수산과학기술진흥원-블루카본 기반 기후변화 적응형 해안조성 기술개발 사업(KIMST-20220526)과 해양수산과학기술진흥원-과학기술기반 해양환경영향평가 기술개발 사업(KIMST-20210427)의 지원을 받아 수행된 연구임.

References

  1. Abba, S. I., Q. B. Pham, G. Saini, N. T. T. Linh, A. N. Ahmed, M. Mohajane, M. Khaledian, R. A. Abdulkadir, and Q. V. Bach(2020), Implementation of Data Intelligence Models Coupled with Ensemble Machine Learning for Prediction of Water Quality Index, Environmental Science and Pollution Research, 27(33), pp. 41524-41539.  https://doi.org/10.1007/s11356-020-09689-x
  2. Bui, D. T., K. Khosravi, J. Tiefenbacher, H. Nguyen, and N. Kazakis(2020), Improving Prediction of Water Quality Indices Using Novel Hybrid Machine-Learning Algorithms, Science of the Total Environment, 721, p. 137612. 
  3. Gaya, M. S., S. I. Abba, M. A. Abdu, A. I. Tukur, M. A. Saleh, P. Esmaili, and N. A. Wahab(2020), Estimation of Water Quality Index Using Artificial Intelligence Approaches and Multi-Linear Regression, IAES International Journal of Artificial Intelligence, 9(1), p. 126. 
  4. Grbcic, L., S. Druzeta, G. Mausa, T. Lipic, D. V. Lusic, M. Alvir, I. Lucin, A. Sikirica, D. Davidovic, V. Travas, D. Kalafatovicm, K. Pikelj, H. Fajkovic, T. Holjevic, and L. Kranjcevic(2022), Coastal Water Quality Prediction Based on Machine Learning with Feature Interpretation and Spatio-Temporal Analysis, Environmental Modelling & Software, 155, p. 105458. 
  5. Haghiabi, A. H., A. H. Nasrolahi, and A. Parsaie(2018), Water Quality Prediction Using Machine Learning Methods, Water Quality Research Journal, 53(1), pp. 3-13.  https://doi.org/10.2166/wqrj.2018.025
  6. Hannan, A. and J. Anmala(2021), Classification and Prediction of Fecal Coliform in Stream Waters Using Decision Trees (DTs) for Upper Green River Watershed, Kentucky, USA, Water, 13(19), p. 2790. 
  7. Huan, J., H. Li, M. Li, and B. Chen(2020), Prediction of Dissolved Oxygen in Aquaculture Based on Gradient Boosting Decision Tree and Long Short-Term Memory Network: A Study of Chang Zhou Fishery Demonstration Base, China, Computers and Electronics in Agriculture, 175, p. 105530. 
  8. Jang, E., J. Im, S. Ha, S. Lee, and Y. G. Park(2016), 'Estimation of Water Quality Index for Coastal Areas in Korea Using GOCI Satellite Data Based on Machine Learning Approaches, Korean Journal of Remote Sensing, 32(3), pp. 221-234  https://doi.org/10.7780/KJRS.2016.32.3.2
  9. Jeon, S. B., H. Y. Oh, and M. H. Jeong(2020), Estimation of Sea Water Quality Level Using Machine Learning. Korea Spatial Information Society, Vol. 28, No. 4, pp. 145-152.  https://doi.org/10.7319/kogsis.2020.28.4.145
  10. Kadam, A. K., V. M. Wagh, A. A. Muley, B. N. Umrikar, and R. N. Sankhua(2019), Prediction of Water Quality Index Using Artificial Neural Network and Multiple Linear Regression Modelling Approach in Shivganga River Basin, India, Modeling Earth Systems and Environment, 5(3), pp. 951-962.  https://doi.org/10.1007/s40808-019-00581-3
  11. Khan, I. U., N. Aslam, R. Alshehri, S. Alzahrani, M. Alghamdi, A. Almalki, and M. Balabeed(2021), Cervical Cancer Diagnosis Model Using Extreme Gradient Boosting and Bioinspired Firefly Optimization, Cervical Cancer Diagnosis Model Using Extreme Gradient Boosting and Bioinspired Firefly Optimization. Scientific Programming. 
  12. Khullar, S. and N. Singh(2021), Machine learning techniques in river water quality modelling: a research travelogue, Water Supply, 21(1), pp. 1-13.  https://doi.org/10.2166/ws.2020.277
  13. Kim, S. B., J. S. Lee, and K. T. Kim(2022), WQI Class Prediction of Sihwa Lake Using Machine Learning-Based Models, J. The Sea: JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY, 27(2), pp. 71-86. 
  14. Kim, Y. H., J. Im, H. K. Ha, J. K. Choi, and S. Ha(2014), Machine learning approaches to coastal water quality monitoring using GOCI satellite data, GIScience & Remote Sensing, 51:2, pp. 158-174.  https://doi.org/10.1080/15481603.2014.900983
  15. Liaw, A. and M. Wiener(2002), Classification and regression by randomForest, R news, 2(3), pp. 18-22. 
  16. Lopez, V., A. Fernandez, S. Garcia, V. Palade, and F. Herrera (2013), An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics, Information Sciences, 250, pp. 113-141.  https://doi.org/10.1016/j.ins.2013.07.007
  17. Mi, Y.(2013), Imbalanced classification based on active learning SMOTE, Research Journal of Applied Sciences, Engineering and Technology, 5(3), pp. 944-949.  https://doi.org/10.19026/rjaset.5.5044
  18. Modaresi, F. and S. Araghinejad(2014), A comparative assessment of support vector machines, probabilistic neural networks, and K-nearest neighbor algorithms for water quality classification, Water resources management, 28(12), pp. 4095-4111.  https://doi.org/10.1007/s11269-014-0730-z
  19. Mountrakis, G., J. Im, and C. Ogole(2011), Support vector machines in remote sensing: A review, ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), pp. 247-259.  https://doi.org/10.1016/j.isprsjprs.2010.11.001
  20. Rho, T. K., T. S. Lee, S. R. Lee, M. S. Choi, C. Park, J. H. Lee, J. Y. Lee, and S. S. Kim(2012), Reference Values and Water Quality Assessment Based on the Regional Environmental Characteristics, The Sea : JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY, Vol. 17, No. 2, pp. 45-58.  https://doi.org/10.7850/jkso.2012.17.2.045
  21. Tanha, J., Y. Abdi, N. Samadi, N. Razzaghi, and M. Asadpour(2020), Boosting methods for multi-class imbalanced data classification: an experimental review, Journal of Big Data, 7(1), pp. 1-47.  https://doi.org/10.1186/s40537-019-0278-0
  22. Uddin, M. G., S. Nash, and A. I. Olbert(2021), A review of water quality index models and their use for assessing surface water quality, Ecological Indicators, 122, p. 107218. 
  23. Uddin, M. G., S. Nash, A. Rahman, and A. I. Olbert(2022a), A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment, Water Research, 219, p. 118532. 
  24. Uddin, M. G., S. Nash, M. T. M. Diganta, A. Rahman, and A. I. Olbert(2022b), Robust machine learning algorithms for predicting coastal water quality index, Journal of Environmental Management, 321, p. 115923.