Comparative Analysis of Machine Learning Algorithms for Forecasting Effluent Chemical Oxygen Demand in Wastewater Treatment Plants

Aalami, M. T., Hejabi, N., Nourani, V., & SAGHEBIAN, S. (2021). Investigation of artificial intelligence approaches capability in predicting the wastewater treatment plant performance (Case study: Tabriz wastewater treatment plant). Amirkabir Journal of Civil Engineering, 53(3), 1033-1048.

Abouzari, M., Pahlavani, P., Izaditame, F., & Bigdeli, B. (2021). Estimating the chemical oxygen demand of petrochemical wastewater treatment plants using linear and nonlinear statistical models–A case study. Chemosphere, 270, 129465.

Aldaghi, T., & Javanmard, S. (2021). The evaluation of wastewater treatment plant performance: A data mining approach. Journal of Engineering, Design and Technology.

An, Q., Rahman, S., Zhou, J., & Kang, J. J. (2023). A comprehensive review on machine learning in healthcare industry: classification, restrictions, opportunities and challenges. Sensors, 23(9), 4178.

Antwi, P., Li, J., Meng, J., Deng, K., Quashie, F. K., Li, J., & Boadi, P. O. (2018). Feedforward neural network model estimating pollutant removal process within mesophilic upflow anaerobic sludge blanket bioreactor treating industrial starch processing wastewater. Bioresource technology, 257, 102-112.

Bagga, P. J., Patel, K. M., Makhesana, M. A., Şirin, Ş., Khanna, N., Krolczyk, G. M., Pala, A. D., & Chauhan, K. C. (2023). Machine vision-based gradient-boosted tree and support vector regression for tool life prediction in turning. The International Journal of Advanced Manufacturing Technology, 126(1), 471-485.

Bagheri, M., Mirbagheri, S. A., Kamarkhani, A. M., & Bagheri, Z. (2016). Modeling of effluent quality parameters in a submerged membrane bioreactor with simultaneous upward and downward aeration treating municipal wastewater using hybrid models. Desalination and Water Treatment, 57(18), 8068-8089.

Bagherzadeh, F., Mehrani, M.-J., Basirifard, M., & Roostaei, J. (2021). Comparative study on total nitrogen prediction in wastewater treatment plant and effect of various feature selection methods on machine learning algorithms performance. Journal of Water Process Engineering, 41, 102033.

Baird, R., Rice, E., & Eaton, A. (2017). Standard methods for the examination of water and wastewaters. Water Environment Federation, Chair Eugene W. Rice, American Public Health Association Andrew D. Eaton, American Water Works Association.

Bekkari, N., & Zeddouri, A. (2019). Using artificial neural network for predicting and controlling the effluent chemical oxygen demand in wastewater treatment plant. Management of Environmental Quality: An International Journal, 30(3), 593-608.

Bhagat, S. K., Tiyasha, T., Awadh, S. M., Tung, T. M., Jawad, A. H., & Yaseen, Z. M. (2021). Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models. Environmental Pollution, 268, 115663.

Granata, F., Papirio, S., Esposito, G., Gargano, R., & De Marinis, G. (2017). Machine learning algorithms for the forecasting of wastewater quality indicators. Water, 9(2), 105.

Güçlü, D., & Dursun, Ş. (2010). Artificial neural network modelling of a large-scale wastewater treatment plant operation. Bioprocess and biosystems engineering, 33, 1051-1058.

Hamed, M. M., Khalafallah, M. G., & Hassanien, E. A. (2004). Prediction of wastewater treatment plant performance using artificial neural networks. Environmental Modelling & Software, 19(10), 919-928.

Hamoda, M. F., Al-Ghusain, I. A., & Hassan, A. H. (1999). Integrated wastewater treatment plant performance evaluation using artificial neural networks. Water Science and Technology, 40(7), 55-65.

Hejabi, N., Saghebian, S. M., Aalami, M. T., & Nourani, V. (2021). Evaluation of the effluent quality parameters of wastewater treatment plant based on uncertainty analysis and post-processing approaches (case study). Water Science and Technology, 83(7), 1633-1648.

Kardam, A., Raj, K. R., Arora, J. K., Srivastava, M. M., & Srivastava, S. (2010). Artificial neural network modeling for sorption of cadmium from aqueous system by shelled Moringa oleifera seed powder as an agricultural waste. Journal of Water Resource and Protection, 2(4), 339.

Karri, R. R., Ravindran, G., & Dehghani, M. H. (2021). Wastewater—sources, toxicity, and their consequences to human health. In Soft computing techniques in solid waste and wastewater management (pp. 3-33). Elsevier.

Kerem, A., & Yuce, E. (2022). Electrical energy recovery from wastewater: prediction with machine learning algorithms. Environmental Science and Pollution Research, 1-14.

Khatri, N., Khatri, K. K., & Sharma, A. (2020). Artificial neural network modelling of faecal coliform removal in an intermittent cycle extended aeration system-sequential batch reactor based wastewater treatment plant. Journal of Water Process Engineering, 37, 101477.

Manu, D., & Thalla, A. K. (2017). Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater. Applied Water Science, 7, 3783-3791.

Matheri, A. N., Mohamed, B., Ntuli, F., Nabadda, E., & Ngila, J. C. (2022). Sustainable circularity and intelligent data-driven operations and control of the wastewater treatment plant. Physics and Chemistry of the Earth, Parts A/B/C, 126, 103152.

Mjalli, F. S., Al-Asheh, S., & Alfadala, H. (2007). Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance. Journal of Environmental Management, 83(3), 329-338.

Mohammad, A. T., Al-Obaidi, M. A., Hameed, E. M., Basheer, B. N., & Mujtaba, I. M. (2020). Modelling the chlorophenol removal from wastewater via reverse osmosis process using a multilayer artificial neural network with genetic algorithm. Journal of Water Process Engineering, 33, 100993.

Nafsin, N., & Li, J. (2022). Prediction of 5‐day biochemical oxygen demand in the Buriganga River of Bangladesh using novel hybrid machine learning algorithms. Water Environment Research, 94(5), e10718.

Ng, K. H., Gan, Y., Cheng, C. K., Liu, K.-H., & Liong, S.-T. (2020). Integration of machine learning-based prediction for enhanced Model’s generalization: Application in photocatalytic polishing of palm oil mill effluent (POME). Environmental Pollution, 267, 115500.

Nourani, V., Elkiran, G., & Abba, S. (2018). Wastewater treatment plant performance analysis using artificial intelligence–an ensemble approach. Water Science and Technology, 78(10), 2064-2076.

Oliveira-Esquerre, K., Mori, M., & Bruns, R. E. (2002). Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis. Brazilian Journal of Chemical Engineering, 19, 365-370.

Park, J., Lechevalier, D., Ak, R., Ferguson, M., Law, K. H., Lee, Y.-T., & Rachuri, S. (2017). Gaussian process regression (GPR) representation in predictive model markup language (PMML). Smart and sustainable manufacturing systems, 1(1), 121.

Qdais, H. A., Hani, K. B., & Shatnawi, N. (2010). Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm. Resources, Conservation and Recycling, 54(6), 359-363.

Rene, E. R., & Saidutta, M. (2008). Prediction of BOD and COD of a refinery wastewater using multilayer artificial neural networks. Journal of Urban and Environmental Engineering, 2(1), 1-7.

Sharafati, A., Asadollah, S. B. H. S., & Hosseinzadeh, M. (2020). The potential of new ensemble machine learning models for effluent quality parameters prediction and related uncertainty. Process Safety and Environmental Protection, 140, 68-78.

Tahraoui, H., Belhadj, A.-E., Triki, Z., Boudellal, N. R., Seder, S., Amrane, A., Zhang, J., Moula, N., Tifoura, A., & Ferhat, R. (2023). Mixed coagulant-flocculant optimization for pharmaceutical effluent pretreatment using response surface methodology and Gaussian process regression. Process Safety and Environmental Protection, 169, 909-927.

Tosun, E., Aydin, K., & Bilgili, M. (2016). Comparison of linear regression and artificial neural network model of a diesel engine fueled with biodiesel-alcohol mixtures. Alexandria Engineering Journal, 55(4), 3081-3089.

Tufaner, F., & Demirci, Y. (2020). Prediction of biogas production rate from anaerobic hybrid reactor by artificial neural network and nonlinear regressions models. Clean Technologies and Environmental Policy, 22, 713-724.

Türkmenler, H., & Murat, P. (2017). Performance assessment of advanced biological wastewater treatment plants using artificial neural networks. International Journal of Engineering Technologies IJET, 3(3), 151-156.

Vyas, M., Modhera, B., Vyas, V., & Sharma, A. (2011). Performance forecasting of common effluent treatment plant parameters by artificial neural network. ARPN Journal of Engineering and Applied Sciences, 6(1), 38-42.

Wang, Y., Cheng, Y., Liu, H., Guo, Q., Dai, C., Zhao, M., & Liu, D. (2023). A review on applications of artificial intelligence in wastewater treatment. Sustainability, 15(18), 13557.

Ye, Z., Yang, J., Zhong, N., Tu, X., Jia, J., & Wang, J. (2020). Tackling environmental challenges in pollution controls using artificial intelligence: A review. Science of the Total Environment, 699, 134279.

Yel, E., & Yalpir, S. (2011). Prediction of primary treatment effluent parameters by Fuzzy Inference System (FIS) approach. procedia computer science, 3, 659-665.