1] Haghiri, S., Daghighi, A., Moharramzadeh, S.(2018). “Optimum coagulant forecasting by modeling jar test experiments using ANNs”. Drinking water engineering and science, 11(1), 1-8.
 Onyelowe, K. C., Jalal, F. E., Onyia, M. E., Onuoha, I. C., Alaneme, G. U. (2021). Application of gene expression programming to evaluate strength characteristics of hydrated-lime-activated rice husk ash-treated expansive soil. Applied computational intelligence and soft computing, 1-17.
 ABIDEEN, M. B. Z. (2016). “Optimization of coagulation process in water treatment plant using statistical approach”. Ph.D. dissertation, Universiti Teknologi Malaysia.
 Zemmouri, H., Drouiche, M., Sayeh, A., Lounici, H., Mameri, N. (2012). Coagulation flocculation test of Keddara's water dam using chitosan and sulfate aluminum. Procedia engineering, 33, 254-260.
 Alshikh, O. (2007). Parameters affecting coagulation/flocculation of drinking water under cold temperatures. University of Windsor (thesis), Canada.
 Zouboulis, A., Traskas, G., Samaras, P. (2008). Comparison of efficiency between poly‐aluminum chloride and aluminum sulphate coagulants during full‐scale experiments in a drinking water treatment plant. Separation science and technology, 43(6), 1507-1519.
 Liu, W. (2016). Enhancement of coagulant dosing control in water and wastewater treatment processes. Ph.D. dissertation, Norwegian University of Life Sciences.
 Wei, N., Zhang, Z., Liu, D., Wu, Y., Wang, J., Wang, Q. (2015). “Coagulation behavior of polyaluminum chloride: Effects of pH and coagulant dosage”. Chinese journal of chemical Engineering, 23(6), 1041-1046.
 Tantipalakul, Y., Palawatwichai, K., Detchakan, T., & Khaisan, J. (2018). “The study of optimal coagulants for water treatment process of Metropolitan Waterworks Authority”. Burapha science journal, 23(1), 207-220.
 Almatin, E., Gholipour, A. (2019). Estimating of optimal dose of PACL for turbidity removing from water. arXiv e-print, arXiv:1904.06421.
 Al-Baidhani, J. H., Alameedee, M. A. (2017). Optimal alum dosage prediction required to treat effluent water turbidity using artificial neural network. International journal of current engineering and technology, 7(4), 1552-1558.
 Alsaeed, R., Alaji, B., Ebrahim, M. (2021). Predicting turbidity and Aluminum in drinking water treatment plants using Hybrid Network (GA-ANN) and GEP. Drinking water engineering and science discussions, 1-17.