Rezaei Kalantari, R., Yari, A. R., Ahmadi, E., Azari, A., Tahmasbi Zade, M., & Gharagazlo, F. (2013). Survey of corrosion and scaling potential in drinking water resources of the villages in Qom province by use of four stability indexes (With Quantitative and qualitative analysis. Archives of Hygiene Sciences, 2(4), 127-134.
[2] Foorginezhad, S., Mohseni-Dargah, M., Firoozirad, K., Aryai, V., Razmjou, A., Abbassi, R.,…Asadnia, M. (2021). Recent advances in sensing and assessment of corrosion in sewage pipelines. Process Safety and Environmental Protection, 147, 192-213.
https://doi.org/110.1016/j.psep.2020.1009.1009
[3] Davis, J. (2000). The effects and economic impact of corrosion. In Corrosion: Understanding the basics (Vol. 164, pp. 62-66). Almere, The Netherlands: ASM International.
[4] Nasr, M., & Zahran, H. F. (2014). Using of pH as a tool to predict salinity of groundwater for irrigation purpose using artificial neural network. The Egyptian Journal of Aquatic Research, 40(2), 111-115.
https://doi.org/110.1016/j.ejar.2014.1006.1005.
[5] Ghaneian, M., Ehrampoush, M., Ghanizadeh, G., & Amrollahi, M. (2008). Survey of corrosion and precipitation potential in dual water distribution system in Kharanagh district of Yazd province. Tolo-e- Behdasht, 7(3-4 (25)), 65-72 (in Persian with English Abstract).
[6] Ghanizadeh, G., & Ghaneeian, M. (2009). Corrosion and precipitation potential of drinking-water distribution systems in military centers. Journal of Military Medicine, 11(3 (41)), 155-160 (in Persian with English Abstract).
[7] Świetlik, J., Raczyk-Stanisławiak, U., Piszora, P., & Nawrocki, J. (2012). Corrosion in drinking water pipes: The importance of green rusts. Water Research, 46(1), 1-10.
http://dx.doi.org/10.1016/j.watres.2011.1010.1006.
[8] Aghazadeh, N., Chitsazan, M., & Golestan, Y. (2017). Hydrochemistry and quality assessment of groundwater in the Ardabil area, Iran. Applied Water Science, 7(7), 3599-3616.
https://doi.org/3510.1007/s13201-13016-10498-13209.
[9] Vairavamoorthy, K., Yan, J., Galgale, H. M., & Gorantiwar, S. D. (2007). IRA-WDS: A GIS-based risk analysis tool for water distribution systems. Environmental Modelling & Software, 22(7), 951-965.
http://dx.doi.org/910.1016/j.envsoft.2006.1005.1027.
[10] Gholizadeh, A., Mokhtari, M., Naimi, N., Shiravand, B., Ehrampoush, M. H., Miri, M., & Ebrahimi, A. (2017). Assessment of corrosion and scaling potential in groundwater resources; a case study of Yazd-Ardakan Plain, Iran. Groundwater for Sustainable Development, 5, 59-65.
https://doi.org/10.1016/j.gsd.2017.1004.1002.
[11] Nayeria, D., Mousavia, S. A., Darvishia, P., Mahmoudia, A., Nooria, E., & Delavaric, S. (2019). Evaluation of water quality and stability in the drinking water distribution network: A case study in the Kermanshah city, Iran. Desalination and Water Treatment, 166, 180-185.
http://dx.doi.org/110.5004/dwt.2019.24623.
[12] Fatemi, A., Eskandari, Z., Gholivand, M. B., & Zarabi, M. (2022). Corrosion and scaling potentials of rural water distribution network in different climate zones of Kermanshah province, Iran. Journal of Applied Research in Water and Wastewater, 9(2), 1-9.
https://doi.org/10.22126/arww.22022.25678.21187.
[13] Siddha, S., & Sahu, P. (2022). Evaluation of corrosivity and scaling properties of groundwater of Central Gujarat for industrial usage. Arabian Journal of Geosciences, 15(9), 891.
https://doi.org/810.1007/s12517-12022-10197-12510.
[14] Alborzi, M., & Abtahi, S. A. R. (2006). Corrosion rate prediction with artificial neural network case: Crude oil distillation overhead systems. Industrial Management Studies, 4(13), 41-66. (in Persian).
[15] Afan, H. A., Yafouz, A., Birima, A. H., Ahmed, A. N., Kisi, O., Chaplot, B., & El-Shafie, A. (2022). Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster. Natural Hazards, 112(2), 1527-1545.
https://doi.org/1510.1007/s11069-11022-05237-11067.
[16] Chaplot, B., & Birbal, P. (2022). Development of stage-discharge rating curve using ANN. International Journal of Hydrology Science and Technology, 14(1), 75-95.
https://doi.org/10.1504/IJHST.2022.123643.
[17] Alaie, M., Zanguie, M., & Zanguie, H. (2015). Forecasting the water quality of Neyshabur plain using MLP networks and adaptive neural fuzzy inference system (ANFIS). National Conference of Environmental Sciences and Engineering, Ahvaz, Iran, 17 February.
[18] Mohammadi, A. A., Ghaderpoori, M., Yousefi, M., Rahmatipoor, M., & Javan, S. (2016). Prediction and modeling of fluoride concentrations in groundwater resources using an artificial neural network: a case study in Khaf. Environmental Health Engineering and Management, 3(4), 217-224.
http://dx.doi.org/210.15171/EHEM.12016.15123
[19] Emami, S., Noruzi-Sarkarabad, R., & Choopan, Y. (2021). Use of artificial neural network and imperialist competitive algorithm to evaluate the groundwater quality of Jolfa Plain for various uses. Amirkabir Journal of Civil Engineering, 53(1), 313-330.
https://doi.org/310.22060/ceej.22018.14258.25605.
[20] Rajaee, T., Khani, S., & Ravansalar, M. (2020). Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: A review. Chemometrics and Intelligent Laboratory Systems, 200, 103978.
http://dx.doi.org/103910.101016/j.chemolab.102020.103978.
[21] Aldrees, A., Khan, M. A., Tariq, M. A. U. R., Mustafa Mohamed, A., Ng, A. W. M., & Bakheit Taha, A. T. (2022). Multi-expression programming (MEP): water quality assessment using water quality indices. Water, 14(6), 947. http://dx.doi.org/910.3390/w14060947.
[22] Kulisz, M., & Kujawska, J. (2021). Application of artificial neural network (ANN) for water quality index (WQI) prediction for the river Warta, Poland. Journal of Physics: Conference Series, 2130. IOP Publishing Ltd.,
https://dx.doi.org/10.1088/1742-6596/2130/1081/012028.
[23] Alqahtani, A., Shah, M. I., Aldrees, A., & Javed, M. F. (2022). Comparative assessment of individual and ensemble machine learning models for efficient analysis of river water quality. Sustainability, 14(3), 1183.
https://doi.org/1110.3390/su14031183.
[24] Beiranvand, S., & Sahraiean, K. (2019). Types of neural networks and their applications The Forth International Conference on Management and Humanities Researches, Tehran, Iran, 14 March.
[25] Angabini, S., Ahmadi, H., Feiznia, S., Vaziri, B., & Ershadi, S. (2014). Suspended sediment concentration estimation using artificial neural networks and fuzzy rule base model case study: Jagin dam. Journal of Applied Sciences Research, 10(14), 12-17.
[26] Muzzammil, M., Alama, J., & Danish, M. (2015). Scour prediction at bridge piers in cohesive bed using gene expression programming. Aquatic Procedia, 4, 789-796.
https://doi.org/710.1016/j.aqpro.2015.1002.1098.
[27] Singley, J., Beaudet, B., & Markey, P. (1984). Corrosion manual for internal corrosion of water distribution systems.
[28] Rossum, J. R., & Merrill, D. T. (1983). An evaluation of the calcium carbonate saturation indexes. Journal American Water Works Association, 75(2), 95-100.
https://doi.org/110.1002/j.1551-8833.1983.tb05075.x.
[29] Dastorani, M. T., Azimi Fashi, K., Talebi, A., & Ekhtesasi, M. R. (2013). Estimation of suspended sediment using artificial neural network (case study: Jamishanwatershed in Kermanshah). Journal of Watershed Management Research, 3(6), 61-74.
[30] Menhaj, M. (2021). Computational Intelligence-Volume I: Fundamentals of Neural Networks. Amirkabir University of Technology Publications, 716.
[31] Reddy, S. (2003). Estimation of watershed runoff using artificial neural networks PhD thesis, IARI, Division of Agricultural Engineering, New Delhi].
[32] Vesta Services, I. (2000). Qnet2000 Shareware. Vesta Services, Inc.
[33] Bagatur, T., & Onen, F. (2014). A predictive model on air entrainment by plunging water jets using GEP and ANN. KSCE Journal of Civil Engineering, 18(1), 304-314.
http://dx.doi.org/310.1007/s12205-12013-10210-12207.
[34] Ferreira, C. (2005). Gene Expression Programming and the Evolution of Computer Programs. In L. Nunes de Castro & F. J. Von Zuben (Eds.), Recent Developments in Biologically Inspired Computing (pp. 82-103). IGI Global.
https://doi.org/10.4018/978-1-59140-312-8.ch005
[35] Wilson, S. (2008). Classifier conditions using gene expression programming (No. IlliGAL Report No. 2008001): University of Illinois at Urbana-Champaign. Proc. 32nd Australasian Computer Science Conference (ACSC 2009), JWellington, New Zealandanuary, 19-23.
[36] Ferreira, C. (2006). Gene expression programming: mathematical modeling by an artificial intelligence (Vol. 21). Springer.
[37] Lopes, H. S., & Weinert, W. R. (2004). EGIPSYS: an enhanced gene expression programming approach for symbolic regression problems. International Journal of Applied Mathematics and Computer Science, 14(3), 375-384.
[38] Gepsoft, L. (2009). Discover the excellent of modelling with GeneXpro Tools 4.0, the best and most intutive modeling software in the market. (Accessed 27 Oct 2009)
http://www.gepsoft.com/.
[39] Antony, A., Low, J. H., Gray, S., Childress, A. E., Le-Clech, P., & Leslie, G. (2011). Scale formation and control in high pressure membrane water treatment systems: A review. Journal of Membrane Science, 383(1-2), 1-16.
http://dx.doi.org/10.1016/j.memsci.2011.1008.1054.
[40] Rafferty, K. (1999). Scaling in geothermal heat pump systems. US Department of Energy Idaho Operations Office785 DOE Place Idaho Falls, ID 83401.
[41] Amiri, M. C. (2018). Water treatment principles (2nd ed.). Arkan Danesh Publication (in Persian).
[42] Davil, M. F., Mahvi, A. H., Norouzi, M., Mazloomi, S., Amarluie, A., Tardast, A., & Karamitabar, Y. (2009). Survey of corrosion and scaling potential produced water from Ilam water treatment plant. World Applied Science Journal, 7(11), 11-24.
[43] Pourzamani, H., Ghazavi, M., & Samani, A. (2005). Evaluation of drinking water quality considering corrosion in industrial park of Oshtorjan, Isfahan 8th National Congress on Environmental Health, Tehran, Iran, 8 November.
[44] Piri Elm, R., Shams, G., Shahmansuri, M., & Farzadkia, M. (2008). Survey of corrosion and scaling potential in drinking water of distribution system of Khoramshahr City with corrosion Index. Quartery Newsletter, 10(13), 15 (in Persian with English Abstract).
[45] Larson, T. E., & Skold, R. V. (1958). Laboratory studies relating mineral quality of water to corrosion of steel and cast iron. Corrosion, 14(6), 43-46.
[46] Zai, K. M., Nawaz, R., Bhatti, I. A., & Bhatti, H. N. (2008). A pHs equation for calcium carbonate scale prediction in cooling water systems. Journal of the Chemical Society of Pakistan, 30(2), 182-185.
[47] Ebrahimi, A., Kamarehie, B., Asgari, G., Seid, M. A., & Roshanaei, G. (2012). Drinking water corrosivity and sediment in the distribution network of Kuhdasht, Iran. Health System Research, 8(3), 480-486 (in Persian with English Abstract).
[48] Taghavi, M., Mohammadi, M. H., Radfard, M., Fakhri, Y., & Javan, S. (2019). Assessment of scaling and corrosion potential of drinking water resources of Iranshahr. MethodsX, 6, 278-283.
https://doi.org/10.1016/j.mex.2019.02.002
[49] Amouei, A., Asgharnia, H., Fallah, H., Yari, A. R., & Mahmoudi, M. (2017). Corrosion and Scaling Potential in Drinking Water Distribution of Babol, Northern Iran Based on the Scaling and Corrosion Indices. Archives of Hygiene Sciences, 6, 1-9.
[50] Shah, M. I., Javed, M. F., & Abunama, T. (2021). Proposed formulation of surface water quality and modelling using gene expression, machine learning, and regression techniques. Environmental Science and Pollution Research, 28, 13202-13220.
http://dx.doi.org/13210.11007/s11356-13020-11490-13209.
[51] Dehghani, R., & Mahmoudi, S. (2014). Performance assessment of gene expression programming model related to chemical parameters to modeling river discharge (case study: Simineh Rood) The First International Congress of Earth Sciences Tehran, Iran, 18 February.
[52] Moasheri, S. (2013). Estimation of the spatial distribution of groundwater quality of Birjand Plain with geostatistical–artificial neural network combined method University of Zabol].
[53] Abbasi Maedeh, P., Mehrdadi, N., Bidhendi, G. N., & Abyaneh, H. Z. (2013). Application of artificial neural network to predict total dissolved solids variations in groundwater of Tehran Plain, Iran. International Journal of Environmental Sustainability, 2(1), 10-20.
[54] Gholabi, M., & Karami, B. (2013). Simulation and prediction of water qualitative parameters using artificial neural network, fuzzy-neural method, and statistical regression (case study: Karoun River, Khouzestan Province) The Ninth International Conference on River Engineering, Ahvaz, Iran, 22 January.
[55] Mirsanjari, M. M., Mohammadyari, F., Basiri, R., & Hamidi pour, F. (2017). Modeling quality parameters EC, SAR and TDS in groundwater using artificial neural network (case study: Mehran Plain and Dehloran). Human and Environment, 15(3), 1-12 (in Persian with English Abstract).
[56] Mirzavand, M., Ghasemieh, H., Akbari, M., & Sadatinejad, S. J. (2015). Artificial neural network (ANN) model for ground water quality simulation (Case study: Kashan aquifer). Journal of Range and Watershed Management, 68(1), 159-171.
https://doi.org/110.22059/jrwm.22015.53892.
[57] Chen, K., Chen, H., Zhou, C., Huang, Y., Qi, X., Shen, R.,…Ren, H. (2020). Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data. Water Research, 171, 115454.
http://dx.doi.org/115410.111016/j.watres.112019.115454.
[58] Birbal, P., Azamathulla, H., Leon, L., Kumar, V., & Hosein, J. (2021). Predictive modelling of the stage–discharge relationship using Gene-Expression Programming. Water Supply, 21(7), 3503-3514.
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