Water Quality Classification by Integration of Attribute-Realization and Support Vector Machine for the Chao Phraya River

Sillberg, Chalisa Veesommai; Kullavanijaya, Pratin; Chavalparit, Orathai

doi:10.12911/22998993/141364

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Tytuł artykułu

Water Quality Classification by Integration of Attribute-Realization and Support Vector Machine for the Chao Phraya River

Autorzy

Sillberg Chalisa Veesommai , Kullavanijaya Pratin , Chavalparit Orathai

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DOI

10.12911/22998993/141364

Warianty tytułu

Języki publikacji

Abstrakty

The water quality index (WQI) is an essential indicator to manage water usage properly. This study aimed at applying a machine learning-based approach integrating attribute-realization (AR) and support vector machine (SVM) algorithm to classify the Chao Phraya River’s water quality. The historical monitoring dataset during 2008-2019 including biological oxygen demand (BOD), conductivity (Cond), dissolved oxygen (DO), faecal coliform bacteria (FCB), total coliform bacteria (TCB), ammonia (NH₃-N), nitrate (NO₃-N), salinity (Sal), suspended solids (SS), total nitrogen (TN), total dissolved solids (TDS), and turbidity (Turb), were processed via four studied steps: data pre-processing by means substituting method, contributing parameter evaluation by recognition pattern study, examination of the mathematic functions for quality classification, and validation of obtained approach. The results showed that NH₃-N, TCB, FCB, BOD, DO, and Sal were the main attributes contributing orderly to water quality classification with confidence values of 0.80, 0.79, 0.78, 0.76, 0.69, and 0.64, respectively. Linear regression was the most suitable function to river water data classification than Sigmoid, Radial basis and Polynomial. The different number of attributes and mathematic functions promoted the different classification performance and accuracy. The validation confirmed that AR-SVM was a potent approach application to classify river water’s quality with 0.86-0.95 accuracy when applied three to six attributes.

Słowa kluczowe

environmental data analysis machine learning SVM support vector machine water quality index WQI

Wydawca

Polskie Towarzystwo Inżynierii Ekologicznej
Lublin University of Technology

Czasopismo

Journal of Ecological Engineering

Rocznik

2021

Tom

Vol. 22, nr 9

Strony

70--86

Opis fizyczny

Bibliogr. 52 poz., rys., tab.

Twórcy

autor

Sillberg Chalisa Veesommai

chalisa.v@ku.th

Department of Environmental Science, Faculty of Environment, Kasetsart University, Bangkok, 10900, Thailand

https://orcid.org/0000-0001-8400-7469

autor

Kullavanijaya Pratin

Pilot Plant Development and Training Institute, Excellent Center of Waste Utilization and Management, King Mongkut’s University of Technology, Thonburi , Bangkhuntien, Bangkok 10150, Thailand

https://orcid.org/0000-0003-1091-6824

autor

Chavalparit Orathai

Department of Environmental Engineering, Faculty of Engineering, Chulalongkorn University Phayathai Rd., Wangmai Pratumwan, Bangkok 10330, Thailand

https://orcid.org/0000-0002-7587-7208

Bibliografia

1. Al-Maolegi M., Arkok B. 2014. An improved Apriori algorithm for association rules. International Journal on Natural Language Computing (IJNLC), 3(1), 21–29.
2. Alpaydin E. 2020. Introduction to machine learning fourth edition. MIT press, United States.
3. Anderson C.W. Turbidity 6.7.2005. USGS National Field Manual for The Collection of Water Quality Data, US Geological Survey.
4. Asquith W.H. 2020. The Use of Support Vectors from Support Vector Machines for Hydrometeorologic Monitoring Network Analyses. Journal of Hydrology, 583, 1–10.
5. Bayram A., Kankal M., Önsoy H. 2012. Estimation of suspended sediment concentration from turbidity measurements using artificial neural networks. Environmental monitoring and assessment, 184(7), 4355–4365.
6. Best M.A., Wither A.W., Coates S. 2007. Dissolved oxygen as a physico-chemical supporting element in the Water Framework Directive. Marine pollution bulletin, 55(1–6), 53–64.
7. Braun A.C., Weidner U., Hinz S. 2011. Support vector machines import vector machines and relevance vector machines for hyperspectral classification—A comparison. In 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 1–4.
8. Bui D.T., Pradhan B., Lofman O., Revhaug I., Dick O.B. 2012. Application of support vector machines in landslide susceptibility assessment for the Hoa Binh province (Vietnam) with kernel functions analysis. In 6th International Congress on Environmental Modelling and Software, 1–9.
9. Chen Y. 2020. Mining of instant messaging data in the Internet of Things based on support vector machine. Computer Communications, 154, 278–287.
10. Charoensuk T., Jantanavanich K., Thanatarnporn W., Srisomporn P. 2019. Monitor and Analyzing Salinity intrusion using Salinity Intrusion Forecast System in the Chao Phraya River During Tropical Pabuk 2019. The 25th National Convention on Civil Engineering, 1–7.
11. Chicco D. & Jurman, G. 2020. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC genomics, 21(1), 1–13.
12. Cude C.G. 2005. Accommodating Change of Bacterial Indicators in Long Term Water Quality Datasets. JAWRA Journal of the American Water Resources Association, 41(1), 47–54.
13. De Figueiredo H.P., de Figueiredo C.R.P., de Souza Barros J.H., Constantino M., Magalhães Filho F.J.C., de Moraes P.M., da Costa R.B.. 2019. Water quality in an urban environmental protection area in the Cerrado Biome, Brazil. Environmental monitoring and assessment, 191(2), 117.
14. Dezfooli D., Hosseini-Moghari S.M., Ebrahimi K., Araghinejad S. 2018. Classification of water quality status based on minimum quality parameters: application of machine learning techniques. Modeling Earth Systems and Environment, 4(1), 311–324.
15. Dietterich T.G. 1997. Machine-learning research. AI magazine, 18(4), 97–97.
16. Fan R.E., Chang K.W., Hsieh C.J., Wang X.R., Lin C.J. 2008. LIBLINEAR: A library for large linear classification. the Journal of machine Learning research, 9, 1871–1874.
17. Francy D.S., Myers D.N, Metzker K.D. 1993. Escherichia coli and fecal-coliform bacteria as indicators of recreational water quality. US Department of the Interior, US Geological Survey, 93(4083), United States of America.
18. Franklin P.A. 2014. Dissolved oxygen criteria for freshwater fish in New Zealand: a revised approach. New Zealand Journal of Marine and Freshwater Research. 48(1), 112–126.
19. Frazier B.E., Naimo T.J., Sandheinrich M.B. 1996. Temporal and vertical distribution of total ammonia nitrogen and un‐ionized ammonia nitrogen in sediment pore water from the upper Mississippi River. Environmental Toxicology and Chemistry: An International Journal, 15(2), 92–99.
20. Gamble A., Babbar-Sebens M. 2012. On the use of multivariate statistical methods for combining in-stream monitoring data and spatial analysis to characterize water quality conditions in the White River Basin, Indiana. USA. Environ. Monit. Assess, 184, 845–875.
21. Gorriz J.M., Ramírez J., Suckling J., Illan I.A., Ortiz A., Martínez-Murcia F.J., Segovia F., Salas-Gonzalez D., Wang S. 2017. Case-based statistical learning: a non-parametric implementation with a conditionalerror rate SVM. IEEE Access, 5, 11468–11478.
22. Gradilla-Hernández M.S., de Anda J., GarciaGonzalez A., Montes C.Y., Barrios-Piña H., RuizPalomino P., Díaz-Vázquez D. 2020. Assessment of the water quality of a subtropical lake using the NSF-WQI and a newly proposed ecosystem specific water quality index. Environmental Monitoring and Assessment, 192, 1–19.
23. Ilayaraja M., Meyyappan T. 2013. Mining medical data to identify frequent diseases using Apriori algorithm. In 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering, 194–199.
24. Inland Water Quality Information System (IWIS) of Pollution Control Department. 2016. Manual for Water Quality Index (WQI) calculation. Pollution Control Department, Bangkok, Thailand.
25. Kalcheva N., Karova M., Penev I. 2020. Comparison of the accuracy of SVM kemel functions in text classification. In 2020 International Conference on Biomedical Innovations and Applications (BIA), 141–145.
26. Kausar N., Samir B.B., Abdullah A., Ahmad I., Hussain M. 2011. A review of classification approaches using support vector machine in intrusion detection. In International Conference on Informatics Engineering and Information Science, 24–34.
27. Keskes H. & Braham A. 2014. DAG SVM and pitch synchronous wavelet transform for induction motor diagnosis. In 7th IET International Conference on Power Electronics, Machines and Drives (PEMD 2014), 1–6.
28. Khalil B., Ou C., Proulx-McInnis S., St-Hilaire A., Zanacic E. 2014. Statistical assessment of the surface water quality monitoring network in Saskatchewan. Water, Air, & Soil Pollution, 225, 1–22.
29. Kurniawan I., Hayder G., Mustafa H.M. 2021. Predicting Water Quality Parameters in a Complex River System. Journal of Ecological Engineering, 22(1), 250–257.
30. Mallasen M., Valenti W.C. 2015. Larval development of the giant river prawn Macrobrachiumrosenbergii at different ammonia concentrations and pH values. Journal of the World Aquaculture Society, 36(1), 32–41.
31. Muharemi F., Logofătu D., Leon F. 2019. Machine learning approaches for anomaly detection of water quality on a real-world data set. Journal of Information and Telecommunication, 3(3), 294–307.
32. Najah A., El-Shafie A., Karim O.A., Jaafar O., ElShafie A.H. 2016. An application of different artificial intelligences techniques for water quality prediction. Int J Phys Sci, 6(22), 5298–5308.
33. Naubi I., Zardari N.H., Shirazi S.M., Ibrahim N.F.B., Baloo L. 2016. Effectiveness of Water Quality Index for Monitoring Malaysian River Water Quality. Polish Journal of Environmental Studies, 25(1), 1–9.
34. Okwuash O., Ndehedehe C.E. 2020. Deep support vector machine for hyperspectral image classification. Pattern Recognition, 103, 1–10.
35. Parra L., Rocher J., Escrivá J., Lloret J. 2018. Design and development of low cost smart turbidity sensor for water quality monitoring in fish farms. Aquacultural Engineering, 81, 10–18.
36. Pollution Control Department, Ministry of Natural Resources and Environment. 2018. Thailand State of Pollution Report 2018. He Company limited, Bangkok, Thailand.
37. Qureshimatva U.M., Maurya R.R., Gamit S.B., Patel R.D., Solanki H.A. 2015. Determination of physico-chemical parameters and water quality index (WQI) of Chandlodia Lake, Ahmedabad, Gujarat, India. J Environ Anal Toxicol, 5(4), 1–6.
38. Ravi K., Ravi V. 2016. Sentiment classification of Hinglish text. In 2016 3rd International Conference on Recent Advances in Information Technology (RAIT), 641–645.
39. Rodrigues A.K., Batista T.V., Moraes R.M., Machado L.S. 2016. A new exponential naive bayes classifier with fuzzy parameters. In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1188–1194.
40. Serrano Balderas E.C. 2017. Preprocessing and analysis of environmental data: Application to the water quality assessment of Mexican rivers. Ph.D. Thesis. University of Montpellier, Montpellier.
41. Shakhman I., Bystriantseva A. 2021. Water Quality Assessment of the Surface Water of the Southern Bug River Basin by Complex Indices. Journal of Ecological Engineering, 22(1), 195–205.
42. Singh K.P., Basant N., Gupta S. 2011. Support vector machines in water quality management. Analytica chimica acta, 3(2), 152–162.
43. Slaughter A.R., Palmer C.G., Muller W.J. 2007. An assessment of two‐step linear regression and multifactor probit analysis as alternatives to acute to chronic ratios in the estimation of chronic response from acute toxicity data to derive water quality guidelines. Integrated Environmental Assessment and Management: An International Journal, 3(2), 193–202.
44. Srivastava G., Kumar P. 2013. Water quality index with missing parameters. International Journal of research in Engineering and Technology, 2(4), 609–614.
45. Talalaj I.A. 2014. Adaptation of water quality index (WQI) for groundwater quality assessment near the landfill site. Journal of Water Chemistry and Technology, 36(3), 144–151.
46. Thammarak K., Rattikansukha C., Kaewrat J., Janta R., Sichum S. 2020. Development web and mobile application and open data platform for water quality management in Pak Phanang river basin. In IOP Conference Series: Earth and Environmental Science, 476(1), 1–8.
47. Tung T.M., Yaseen Z.M. 2020. A survey on river water quality modelling using artificial intelligence models: 2000–2020. Journal of Hydrology, 585, 1–62.
48. US Environmental Protection Agency (USEPA). 1986. Ambient water quality criteria for bacteria 1986. EPA 440/5-84-002. Office of Water Regulations and Standards, US Environmental Protection Agency, Washington, DC.
49. Wei S., Wang T., Li Y. 2017. Influencing factors and prediction of carbon dioxide emissions using factor analysis and optimized least squares support vector machine. Environmental Engineering Research, 22(2), 175–185.
50. Yan F., Liu L., Li Y., Zhang Y., Chen M., Xing X. 2015. A dynamic water quality index model based on functional data analysis. Ecological indicators, 57, 249–258.
51. Ye Y., Chiang C.C. 2016. A parallel apriori algorithm for frequent itemsets mining. In Fourth International Conference on Software Engineering Research, Management and Applications, 87–94.
52. Zhou D.X., Jetter K. 2006. Approximation with polynomial kernels and SVM classifiers. Advances in Computational Mathematics, 25(1–3), 323–344.

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