Stacking Artificial Intelligence Models for Predicting Water Quality Parameters in Rivers

• Autorzy: Almadani Mohammad , Kheimi Marwan

Identyfikatory

DOI

10.12911/22998993/156663

• Języki publikacji: EN

Abstrakty

EN

Scrutinizing the changes in the quality of river water is one of the main factors of monitoring the quality of natural flows, which plays a crucial role in the sustainable management of these ecosystems. The concentration of dissolved oxygen (DO) in river water is one of the most important indicators of quality management in such water bodies. From an environmental point of view, exceeding the permissible and natural decay capacity of pollutants in natural streams leads to a decrease in DO and, consequently, causes serious risks for the survival of aquatic life in related ecosystems. Hence, in the present study, 10 daily variables with the amount of dissolved oxygen on the same day were collected and evaluated from Allen County. Moreover, half of these variables were chosen as effective inputs to the model based on statistical analysis, so as to calculate the dissolved oxygen concentration parameter. Modeling with artificial intelligence approaches was implemented in the form of four individual methods: ANFIS-PSO, OS-ELM, Bagging-RF and Boosting CART, and two ensemble-stacking methods: SMA and Meta-learner MLP. The outcomes of estimating the DO with RMSE, MAE, GRI, r, and MBE criteria and marginal-scatter and subject profile diagrams were discussed. Moreover, the efficiency of the models in estimating the outlier of the observational data was scrutinized by subject profile diagram. Finally, it was found that the Meta-learner MLP model with RMSE of 0.965 mg/L had improvement in performance by 8.8%, 8.9%, 22.3%, 24.9% and 27.6%, respectively, compared to SMA, Boosting CART, Bagging-RF, ANFIS-PSO and OS-ELM methods. This remarkable improvement led to recommendations for using stacking techniques in water quality modeling and simulation.

Słowa kluczowe

EN

dissolved oxygen; water quality; ensemble-stacking model; meta-learner

• Wydawca: Polskie Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology
• Czasopismo: Journal of Ecological Engineering
• Rocznik: 2023
• Tom: Vol. 24, nr 2
• Strony: 152--164
• Opis fizyczny: Bibliogr. 38 poz., rys., tab.

Twórcy

autor

Almadani Mohammad

• Department of Civil Engineering, Faculty of Engineering – Rabigh Branch, King Abdulaziz University, Jeddah 21589, Saudi Arabia

• https://orcid.org/0000-0003-2017-4932

autor

Kheimi Marwan

• Department of Civil Engineering, Faculty of Engineering – Rabigh Branch, King Abdulaziz University, Jeddah 21589, Saudi Arabia

• https://orcid.org/0000-0003-3962-9837

Bibliografia

• 1. Abazi A.S., Gashi B., Spahiu M.H., Bytyçi P., Dreshaj A. 2022. Analysis of the impact of ferronicel industrial activity on Drenica River quality. Journal of Ecological Engineering, 23(7), 312–322.
• 2. Ahmed A.N., Othman F.B., Afan H.A., Ibrahim R.K., Fai C.M., Hossain M.S., Ehteram M., Elshafie A. 2019. Machine learning methods for better water quality prediction. Journal of Hydrology, 578, 124084.
• 3. Benedini M., Tsakiris G. 2013. Water Quality Modelling for Rivers and Streams. Springer Science+Business Media Dordrecht, 290.
• 4. Bui D.T., Khosravi K., Tiefenbacher J., Nguyen H., Kazakis N. 2020. Improving prediction of water quality indices using novel hybrid machine-learning algorithms. Science of The Total Environment, 721, 137612.
• 5. Barzegar R., Asghari Moghaddam A. 2016. Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction. Modeling Earth Systems and Environment, 2, 26.
• 6. Breiman L. 2001. Random forests. Machine Learning, 45, 5–32.
• 7. Dehghani R., Torabi Poudeh H., Izadi Z. 2022. Dissolved oxygen concentration predictions for running waters with using hybrid machine learning techniques. Modeling Earth Systems and Environment, 8, 2599–2613.
• 8. Drozdov I.Y., Aleksakhin A.V., Aleksakhina Y.V., Petrusevich D.A. 2021. Mathematical models of water pollution evaluation. In IOP Conference Series: Earth and Environmental Science 684(1), 012026, IOP Publishing.
• 9. Fadaee M., Mahdavi‐Meymand A., Zounemat‐Kermani M. 2020. Seasonal short‐term prediction of dissolved oxygen in rivers via nature‐inspired algorithms. CLEAN–Soil, Air, Water, 48(2), 1900300.
• 10. Fürnkranz J., Gamberger D., Lavrač N. 2012. Foundations of Rule Learning. Springer-Verlag Berlin Heidelberg, 344.
• 11. Guo H., Huang J.J., Zhu X., Wang B., Tian S., Xu W., Mai Y. 2021. A generalized machine learning approach for dissolved oxygen estimation at multiple spatiotemporal scales using remote sensing. Environmental Pollution, 288, 117734.
• 12. Haghiabi A.H., Nasrolahi A.H., Parsaie A. 2018. Water quality prediction using machine learning methods. Water Quality Research Journal, 53(1), 3–13.
• 13. Huang G.-B., Zhu Q.-Y., Siew C.-K. 2006. Extreme learning machine: Theory and applications. Neurocomputing, 70(1–3), 489–501.
• 14. Jachner S., Gerald van den Boogaart K., Petzoldt T. 2007. Statistical methods for the qualitative assessment of dynamic models with time delay (R Package qualV). Journal of Statistical Software, 22(8), 1–30.
• 15. Jacovides C.P., Kontoyiannis H. 1995. Statistical procedures for the evaluation of evapotranspiration computing models. Agricultural Water Management, 27(3–4), 365–371.
• 16. Jang J.-S.R. 1993. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685.
• 17. Kim Y.H., Im J., Ha H.K., Choi J.K., Ha S. 2014. Machine learning approaches to coastal water quality monitoring using GOCI satellite data. GIScience & Remote Sensing, 51(2), 158–174.
• 18. Kisi O., Parmar K.S. 2016. Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution. Journal of Hydrology, 534, 104–112.
• 19. Leong W.C., Bahadori A., Zhang J., Ahmad Z. 2021. Prediction of water quality index (WQI) using support vector machine (SVM) and least square-support vector machine (LS-SVM). International Journal of River Basin Management, 19(2), 149–156.
• 20. Li J., Abdulmohsin H.A., Hasan S.S., Kaiming L., Al-Khateeb B., Ghareb M.I., Mohammed M.N. 2019. Hybrid soft computing approach for determining water quality indicator: Euphrates River. Neural Computing and Applications, 31(3), 827–837.
• 21. Liang N.-Y., Huang G.-B., Saratchandran P., Sundararajan N. 2006. A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Transactions on Neural Networks, 17(6), 1411–1423.
• 22. Lu H., Ma X. 2020. Hybrid decision tree-based machine learning models for short-term water quality prediction. Chemosphere, 249, 126169.
• 23. Lusiana E.D., Mahmudi M., Hutahaean S.M., Darmawan A., Buwono N.R., Arsad S., Musa M. 2022. A multivariate technique to develop hybrid water quality index of the Bengawan Solo River, Indonesia. Journal of Ecological Engineering, 23(2), 123–131.
• 24. Najah A., Elshafie A., Karim O.A., Jaffar O. 2009. Prediction of Johor River water quality parameters using artificial neural networks. European Journal of Scientific Research, 28(3), 422–435.
• 25. Najah A., El-Shafie A., Karim O.A., El-Shafie A.H. 2014. Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring. Environmental Science and Pollution Research, 21, 1658–1670.
• 26. Pham Q.B., Mohammadpour R., Linh N.T.T., Mohajane M., Pourjasem A., Sammen S.S., Anh D.T., Nam V.T. 2021. Application of soft computing to predict water quality in wetland. Environmental Science and Pollution Research, 28(1), 185–200.
• 27. Raheli B., Aalami M.T., El-Shafie A., Ghorbani M.A., 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, 76, 503.
• 28. Rahutami S., Said M., Ibrahim E., Herpandi. 2022. Actual status assessment and prediction of the Musi River water quality, Palembang, South Sumatra, Indonesia. Journal of Ecological Engineering, 23(10), 68–79.
• 29. Sarkar A., Pandey P. 2015. River water quality modelling using artificial neural network technique. Aquatic procedia, 4, 1070–1077.
• 30. Schaffner M., Bader H.P., Scheidegger R. 2009. Modeling the contribution of point sources and non-point sources to Thachin River water pollution. Science of the Total Environment, 407(17), 4902–4915.
• 31. Shiri N., Shiri J., Yaseen Z.M., Kim S., Chung I.M., Nourani V., Zounemat-Kermani M. 2021. Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios. Plos one, 16(5), e0251510.
• 32. Singh K.P., Basant A., Malik A., Jain G. 2009. Artificial neural network modeling of the river water quality—A case study. Ecological Modelling, 220(6), 888–895.
• 33. USGS (U.S. Geological Survey). 2022. Available at: 〈https://nwis.waterdata.usgs.gov/in/nwis/dv/?site_no=04183038&agency_cd=USGS&referred_module=sw 〉 (accessed 26 March 2022).
• 34. Varol M. 2020. Use of water quality index and multivariate statistical methods for the evaluation of water quality of a stream affected by multiple stressors: A case study. Environmental Pollution, 266, 115417.
• 35. Yu J.-W., Kim J.-S., Li X., Jong Y.-C., Kim K.-H., Ryang G.-I. 2022. Water quality forecasting based on data decomposition, fuzzy clustering and deep learning neural network. Environmental Pollution, 303, 119136.
• 36. Zounemat-Kermani M., Seo Y., Kim S., Ghorbani M.A., Samadianfard S., Naghshara S., Kim N.W., 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, 9(12), 2534.
• 37. Zounemat-Kermani M., Batelaan O., Fadaee M., Hinkelmann R. 2021a. Ensemble machine learning paradigms in hydrology: A review. Journal of Hydrology, 598, 126266.
• 38. Zounemat-Kermani M., Alizamir M., Fadaee M., Sankaran Namboothiri A., Shiri J. 2021b. Online sequential extreme learning machine in river water quality (turbidity) prediction: A comparative study on different data mining approaches. Water and Environment Journal, 35(1), 335–348.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).