Perspectives on water quality analysis emphasizing indexing, modeling, and application of artificial intelligence for comparison and trend forecasting

Abstract

Freshwater essential for civilization faces risk from untreated effluents discharged by industries, agriculture, urban areas, and other sources. Increasing demand and abstraction of freshwater deteriorate the pollution scenario more. Hence, water quality analysis (WQA) is an important task for researchers and policymakers to maintain sustainability and public health. This study aims to gather and discuss the methods used for WQA by the researchers, focusing on their advantages and limitations. Simultaneously, this study compares different WQA methods, discussing their trends and future directions. Publications from the past decade on WQA are reviewed, and insights are explored to aggregate them in particular categories. Three major approaches, namely—water quality indexing, water quality modeling (WQM) and artificial intelligence-based WQM, are recognized. Different methodologies adopted to execute these three approaches are presented in this study, which leads to formulate a comparative discussion. Using statistical operations and soft computing techniques have been done by researchers to combat the subjectivity error in indexing. To achieve better results, WQMs are being modified to incorporate the physical processes influencing water quality more robustly. The utilization of artificial intelligence was primarily restricted to conventional networks, but in the last 5 years, implications of deep learning have increased rapidly and exhibited good results with the hybridization of feature extracting and time series modeling. Overall, this study is a valuable resource for researchers dedicated to WQA.

Keywords

deep learning / water quality analysis / water quality index / water quality modeling

References

[1] Abbaspour, K. C., Rouholahnejad, E., Vaghefi, S., Srinivasan, R., Yang, H., & Kløve, B. (2015). A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. Journal of Hydrology, 524, 733-752.

[2] Afshar, A., Feizi, F., Moghadam, A. Y., & Saadatpour, M. (2017). Enhanced CE-QUAL-W2 model to predict the fate and transport of volatile organic compounds in water body: Gheshlagh reservoir as case study. Environmental Earth Sciences, 76, 803.

[3] Akkoyunlu, A., & Karaaslan, Y. (2015). Assessment of improvement scenario for water quality in Mogan Lake by using the AQUATOX model. Environmental Science and Pollution Research, 22(18), 14349-14357.

[4] Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J., Srinivasan, R., Santhi, C., Harmel, R. D., van Griensven, A., Van Liew, M. W., Kannan, N., & Jha, M. K. (2012). SWAT: Model use, calibration, and validation. Transactions of the ASABE, 55(4), 1491-1508.

[5] Arnold, J. G., Srinivasan, R., Muttiah, R. S., & Williams, J. R. (1998). Large area hydrologic modeling and assessment part I: Model development 1. JAWRA Journal of the American Water Resources Association, 34(1), 73-89.

[6] Arora, S., & Keshari, A. K. (2021). Pattern recognition of water quality variance in Yamuna River (India) using hierarchical agglomerative cluster and principal component analyses. Environmental Monitoring and Assessment, 193(8), 494.

[7] Aydin, H., Ustaoğlu, F., Tepe, Y., & Soylu, E. N. (2021). Assessment of water quality of streams in northeast Turkey by water quality index and multiple statistical methods. Environmental Forensics, 22(1–2), 270-287.

[8] Baek, S. S., Pyo, J., & Chun, J. A. (2020). Prediction of water level and water quality using a CNN-LSTM combined deep learning approach. Water, 12(12), 3399.

[9] Bai, J., Zhao, J., Zhang, Z., & Tian, Z. (2022). Assessment and a review of research on surface water quality modeling. Ecological Modelling, 466, 109888.

[10] Barzegar, R., Aalami, M. T., & Adamowski, J. (2020). Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model. Stochastic Environmental Research and Risk Assessment, 34(2), 415-433.

[11] Benicio, S. H. M., Basso, R. E., & Formiga, K. T. M. (2024). Global applications of the CE-QUAL-W2 model in reservoir eutrophication: A systematic review and perspectives for Brazil. Water, 16(24), 3556.

[12] Bi, J., Yuan, H., Li, S., Zhang, K., Zhang, J., & Zhou, M. (2024). Arima-based and multiapplication workload prediction with wavelet decomposition and Savitzky–Golay filter in clouds. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54(4), 2495-2506.

[13] Bordalo, A. A., Teixeira, R., & Wiebe, W. J. (2006). A water quality index applied to an international shared river basin: The case of the Douro River. Environmental Management, 38, 910-920.

[14] Botle, A., Salgaonkar, S., Tiwari, R., Ambadekar, S., & Barabde, G. R. (2023). Brief status of contamination in surface water of rivers of India by heavy metals: A review with pollution indices and health risk assessment. Environmental Geochemistry and Health, 45(6), 2779-2801.

[15] Bouchard, D., Knightes, C., Chang, X., & Avant, B. (2017). Simulating multiwalled carbon nanotube transport in surface water systems using the Water Quality Analysis Simulation Program (WASP). Environmental Science & Technology, 51(19), 11174-11184.

[16] Brown, R. M., McClelland, N. I., Deininger, R. A., & Tozer, R. G. (1970). A water quality index-do we dare. Water and Sewage Works, 117(10), 339-343.

[17] Burigato Costa, C. M. S., da Silva Marques, L., Almeida, A. K., Leite, I. R., & De Almeida, I. K. (2019). Applicability of water quality models around the world—a review. Environmental Science and Pollution Research, 26, 36141-36162.

[18] Cashel, F. S., Knightes, C. D., Lupo, C., Iott, T., Streich, K., Conville, C. J., Bridges, T. W., & Dombroski, I. (2024). Using monitoring and mechanistic modeling to improve understanding of eutrophication in a shallow New England estuary. Journal of Environmental Management, 355, 120478.

[19] CCME. (2001). Canadian water quality guidelines for the protection of aquatic life: CCME Water Quality Index 1.0 (Technical Report). Canadian environmental quality guidelines, 1999, Canadian Council of Ministers of the Environment.

[20] Chen, L., Yang, Z., & Liu, H. (2016). Assessing the eutrophication risk of the Danjiangkou Reservoir based on the EFDC model. Ecological Engineering, 96, 117-127.

[21] Chidiac, S., El Najjar, P., Ouaini, N., El Rayess, Y., & El Azzi, D. (2023). A comprehensive review of water quality indices (WQIs): History, models, attempts and perspectives. Reviews in Environmental Science and Bio/Technology, 22(2), 349-395.

[22] Cole, T. M., & Wells, S. A. (2006). CE-QUAL-W2: A two-dimensional, laterally averaged, hydrodynamic and water quality model, version 3.5 (Instruction Report EL-06-1). US Army Engineering and Research Development Center.

[23] Cude, C. G. (2001). Oregon water quality index a tool for evaluating water quality management effectiveness. JAWRA Journal of the American Water Resources Association, 37(1), 125-137.

[24] Dilmi, S., & Ladjal, M. (2021). A novel approach for water quality classification based on the integration of deep learning and feature extraction techniques. Chemometrics and Intelligent Laboratory Systems, 214, 104329.

[25] Ding, F., Zhang, W., Cao, S., Hao, S., Chen, L., Xie, X., Li, W., & Jiang, M. (2023). Optimization of water quality index models using machine learning approaches. Water Research, 243, 120337.

[26] Dobbins, W. E. (1964). BOD and oxygen relationships in streams. Journal of the Sanitary Engineering Division, 90(3), 53-78.

[27] Dojlido, J., Raniszewski, J., & Woyciechowska, J. (1994). Water quality index applied to rivers in the Vistula river basin in Poland. Environmental Monitoring and Assessment, 33, 33-42.

[28] Dutta, S., Dwivedi, A., & Suresh Kumar, M. (2018). Use of water quality index and multivariate statistical techniques for the assessment of spatial variations in water quality of a small river. Environmental Monitoring and Assessment, 190(12), 718.

[29] Elçi, A., Ayaz, S., & Aynur, Ş. (2018). Simulating the impact of water quality improvement measures for nutrient-sensitive river basins with the Aquatox model. EPiC Series in Engineering, 3, 651-658.

[30] Epelde, A. M., Cerro, I., Sánchez-Pérez, J. M., Sauvage, S., Srinivasan, R., & Antigüedad, I. (2015). Application of the SWAT model to assess the impact of changes in agricultural management practices on water quality. Hydrological Sciences Journal, 60(5), 1-19.

[31] Ewaid, S. H., & Abed, S. A. (2017). Water quality index for Al-Gharraf River, Southern Iraq. Egyptian Journal of Aquatic Research, 43(2), 117-122.

[32] Fang, S., Deitch, M. J., Gebremicael, T. G., Angelini, C., & Ortals, C. J. (2024). Identifying critical source areas of non-point source pollution to enhance water quality: Integrated SWAT modeling and multi-variable statistical analysis to reveal key variables and thresholds. Water Research, 253, 121286.

[33] Fathi, E., Zamani-Ahmadmahmoodi, R., & Zare-Bidaki, R. (2018). Water quality evaluation using water quality index and multivariate methods, Beheshtabad river, Iran. Applied Water Science, 8(7), 210.

[34] Gani, M. A., Sajib, A. M., Siddik, M. A., & Md Moniruzzaman, M. (2023). Assessing the impact of land use and land cover on river water quality using water quality index and remote sensing techniques. Environmental Monitoring and Assessment, 195(4), 449.

[35] Gao, L., & Li, D. (2014). A review of hydrological/water-quality models. Frontiers of Agricultural Science and Engineering, 1(4), 267.

[36] Gao, Q., He, G., Fang, H., Bai, S., & Huang, L. (2018). Numerical simulation of water age and its potential effects on the water quality in Xiangxi Bay of Three Gorges Reservoir. Journal of Hydrology, 566, 484-499.

[37] Gazzaz, N. M., Yusoff, M. K., Ramli, M. F., Juahir, H., & Aris, A. Z. (2015). Artificial neural network modeling of the water quality index using land use areas as predictors. Water Environment Research, 87(2), 99-112.

[38] Giri, S. (2021). Water quality prospective in twenty first century: Status of water quality in major river basins, contemporary strategies and impediments: A review. Environmental Pollution, 271, 116332.

[39] Goher, M. E., Hassan, A. M., Abdel-Moniem, I. A., Fahmy, A. H., & El-Sayed, S. M. (2014). Evaluation of surface water quality and heavy metal indices of Ismailia Canal, Nile River, Egypt. Egyptian Journal of Aquatic Research, 40(3), 225-233.

[40] Golomb, D., Ryan, D., Underhill, J., Wade, T., & Zembar, S. (1997). Atmospheric deposition of toxics onto Massachusetts Bay—II. Polycyclic aromatic hydrocarbons. Atmospheric Environment, 31(9), 1361-1368.

[41] Gong, R., Xu, L., Wang, D., Li, H., & Xu, J. (2016). Water quality modeling for a typical urban lake based on the EFDC model. Environmental Modeling & Assessment, 21, 643-655.

[42] Gong, X., Bian, J., Wang, Y., Jia, Z., & Wan, H. (2019). Evaluating and predicting the effects of land use changes on water quality using SWAT and CA–Markov models. Water Resources Management, 33, 4923-4938.

[43] Gough, D. I. (1969). Incremental stress under a two-dimensional artificial lake. Canadian Journal of Earth Sciences, 6(5), 1067-1075.

[44] Gupta, S., & Gupta, S. K. (2021). A critical review on water quality index tool: Genesis, evolution and future directions. Ecological Informatics, 63, 101299.

[45] Hamrick, J. M. (1996). User's manual for the environmental fluid dynamics computer code. (Special Reports in Applied Marine Science and Ocean Engineering [SRAMSOE] No. 331). Virginia Institute of Marine Science, College of William and Mary. https://doi.org/10.21220/V5M74W

[46] Han, M., Su, Z., & Na, X. (2023). Predict water quality using an improved deep learning method based on spatiotemporal feature correlated: A case study of the Tanghe Reservoir in China. Stochastic Environmental Research and Risk Assessment, 37(7), 2563-2575.

[47] Horton, R. K. (1965). An index number system for rating water quality. Journal of the Water Pollution Control Federation, 37(3), 300-306.

[48] House, M. A. (1989). A water quality index for river management. Water and Environment Journal, 3(4), 336-344.

[49] Ighalo, J. O., Adeniyi, A. G., & Marques, G. (2021). Artificial intelligence for surface water quality monitoring and assessment: A systematic literature analysis. Modeling Earth Systems and Environment, 7(2), 669-681.

[50] Kadam, A. K., Wagh, V. M., Muley, A. A., Umrikar, B. N., & Sankhua, R. N. (2019). Prediction of water quality index using artificial neural network and multiple linear regression modelling approach in Shivganga River basin, India. Modeling Earth Systems and Environment, 5, 951-962.

[51] Khullar, S., & Singh, N. (2022). Water quality assessment of a river using deep learning Bi-LSTM methodology: Forecasting and validation. Environmental Science and Pollution Research, 29(9), 12875-12889.

[52] Kim, J., Lee, T., & Seo, D. (2017). Algal bloom prediction of the lower Han River, Korea using the EFDC hydrodynamic and water quality model. Ecological Modelling, 366, 27-36.

[53] Kim, J., Seo, D., Jang, M., & Kim, J. (2021). Augmentation of limited input data using an artificial neural network method to improve the accuracy of water quality modeling in a large lake. Journal of Hydrology, 602, 126817.

[54] Koçer, M. A. T., & Sevgili, H. (2014). Parameters selection for water quality index in the assessment of the environmental impacts of land-based trout farms. Ecological Indicators, 36, 672-681.

[55] Lee, J., Woo, S. Y., Kim, Y. W., Kim, S. J., Pyo, J., & Cho, K. H. (2022). Dynamic calibration of phytoplankton blooms using the modified SWAT model. Journal of Cleaner Production, 343, 131005.

[56] Lee, S., Kim, D., McCarty, G. W., Anderson, M., Gao, F., Lei, F., Moglen, G. E., Zhang, X., Yen, H., Qi, J., Crow, W., Yeo, I. Y., & Sun, L. (2024). Spatial calibration and uncertainty reduction of the SWAT model using multiple remotely sensed data. Heliyon, 10(10), e30923.

[57] Li, L., Jiang, P., Xu, H., Lin, G., Guo, D., & Wu, H. (2019). Water quality prediction based on recurrent neural network and improved evidence theory: A case study of Qiantang River, China. Environmental Science and Pollution Research, 26, 19879-19896.

[58] Liou, S. M., Lo, S. L., & Wang, S. H. (2004). A generalized water quality index for Taiwan. Environmental Monitoring and Assessment, 96, 35-52.

[59] Lkr, A., Singh, M. R., & Puro, N. (2020). Assessment of water quality status of Doyang river, Nagaland, India, using water quality index. Applied Water Science, 10(1), 46.

[60] Lombardo, A., Franco, A., Pivato, A., & Barausse, A. (2015). Food web modeling of a river ecosystem for risk assessment of down-the-drain chemicals: A case study with AQUATOX. Science of the Total Environment, 508, 214-227.

[61] Ma, K., Lu, Y., Zhang, Y., & Zhang, Y. (2024). Trend of PFAS concentrations and prediction of potential risks in Taihu Lake of China by AQUATOX. Environmental Research, 251, 118707.

[62] Martin, J. L., & Wool, T. A. (2017). Supplement to Water Analysis Simulation Program User Documentation, Wasp Sediment Diagenesis Routines: Model Theory and User's Guide. U.S. EPA, Region 4 Water Management Division.

[63] Masoumi, F., Najjar-Ghabel, S., & Salimi, N. (2021). Automatic calibration of the two-dimensional hydrodynamic and water quality model using sequential uncertainty fitting approach. Environmental Monitoring and Assessment, 193, 67.

[64] Mbuh, M. J., Mbih, R., & Wendi, C. (2019). Water quality modeling and sensitivity analysis using Water Quality Analysis Simulation Program (WASP) in the Shenandoah River watershed. Physical Geography, 40(2), 127-148.

[65] Misaghi, F., Delgosha, F., Razzaghmanesh, M., & Myers, B. (2017). Introducing a water quality index for assessing water for irrigation purposes: A case study of the Ghezel Ozan River. Science of the Total Environment, 589, 107-116.

[66] Mishra, A. P., Singh, S., Jani, M., Singh, K. A., Pande, C. B., & Varade, A. M. (2024). Assessment of water quality index using Analytic Hierarchy Process (AHP) and GIS: A case study of a struggling Asan River. International Journal of Environmental Analytical Chemistry, 104(5), 1159-1171.

[67] Mokarram, M., Pourghasemi, H. R., & Pham, T. M. (2024). Enhancing water quality monitoring through the integration of deep learning neural networks and fuzzy method. Marine Pollution Bulletin, 206, 116698.

[68] Morselli, L., Olivieri, P., Brusori, B., & Passarini, F. (2003). Soluble and insoluble fractions of heavy metals in wet and dry atmospheric depositions in Bologna, Italy. Environmental Pollution, 124(3), 457-469.

[69] Moses, S. A., Janaki, L., Joseph, S., & Joseph, J. (2015). Water quality prediction capabilities of WASP model for a tropical lake system. Lakes & Reservoirs: Research & Management, 20(4), 285-299.

[70] Mourhir, A., Rachidi, T., & Karim, M. (2014). River water quality index for Morocco using a fuzzy inference system. Environmental Systems Research, 3, 21.

[71] Naik, M. R., Mahanty, B., Sahoo, S. K., Jha, V. N., & Sahoo, N. K. (2022). Assessment of groundwater geochemistry using multivariate water quality index and potential health risk in industrial belt of central Odisha, India. Environmental Pollution, 303, 119161.

[72] Nath Roy, B., Roy, H., Rahman, K. S., Mahmud, F., Bhuiyan, M. M. K., Hasan, M., Bhuiyan, A. A. K., Hasan, M., Mahbub, M. S., Jahedi, R. M., & Islam, M. S. (2024). Principal component analysis incorporated water quality index modeling for Dhaka-based rivers. City and Environment Interactions, 23, 100150.

[73] Nazeer, S., Hashmi, M. Z., & Malik, R. N. (2014). Heavy metals distribution, risk assessment and water quality characterization by water quality index of the River Soan, Pakistan. Ecological Indicators, 43, 262-270.

[74] Niknam, A. R. R., Sabaghzadeh, M., Barzkar, A., & Shishebori, D. (2024). Comparing ARIMA and various deep learning models for long-term water quality index forecasting in Dez River, Iran. Environmental Science and Pollution Research, 32, 10206-10222. https://doi.org/10.1007/s11356-024-32228-x

[75] Nishat, M. H., Khan, M. H. R. B., Ahmed, T., Hossain, S. N., Ahsan, A., El-Sergany, M. M., Shafiquzzaman, M., Imteaz, M. A., & Alresheedi, M. T. (2025). Comparative analysis of machine learning models for predicting water quality index in Dhaka's rivers of Bangladesh. Environmental Sciences Europe, 37(1), 31.

[76] Njuguna, S. M., Onyango, J. A., Githaiga, K. B., Gituru, R. W., & Yan, X. (2020). Application of multivariate statistical analysis and water quality index in health risk assessment by domestic use of river water. Case study of Tana River in Kenya. Process Safety and Environmental Protection, 133, 149-158.

[77] Obin, N., Tao, H., Ge, F., & Liu, X. (2021). Research on water quality simulation and water environmental capacity in Lushui River based on WASP model. Water, 13(20), 2819.

[78] Oni, O., & Fasakin, O. (2016). The use of water quality index method to determine the potability of surface water and groundwater in the vicinity of a municipal solid waste dumpsite in Nigeria. American Journal of Engineering Research, 5(10), 96-101.

[79] Ott, W. R. (1978). Water Quality Indices: A Survey of Indices Used in the United States. Environmental Protection Agency, Office of Research and Development, Office of Monitoring and Technical Support.

[80] Pak, H. Y., Chuah, C. J., Tan, M. L., Yong, E. L., & Snyder, S. A. (2021). A framework for assessing the adequacy of Water Quality Index–Quantifying parameter sensitivity and uncertainties in missing values distribution. Science of the Total Environment, 751, 141982.

[81] Park, R. A., Clough, J. S., & Wellman, M. C. (2008). AQUATOX: Modeling environmental fate and ecological effects in aquatic ecosystems. Ecological Modelling, 213(1), 1-15.

[82] Parween, S., Siddique, N. A., Mahammad Diganta, M. T., Olbert, A. I., & Uddin, M. G. (2022). Assessment of urban river water quality using modified NSF water quality index model at Siliguri city, West Bengal, India. Environmental and Sustainability Indicators, 16, 100202.

[83] Pesce, S. (2000). Use of water quality indices to verify the impact of Córdoba City (Argentina) on Suquı́a River. Water Research, 34(11), 2915-2926.

[84] Poor, N., Pribble, R., & Greening, H. (2001). Direct wet and dry deposition of ammonia, nitric acid, ammonium and nitrate to the Tampa Bay Estuary, FL, USA. Atmospheric Environment, 35(23), 3947-3955.

[85] Prasad, D. V. V., Venkataramana, L. Y., Kumar, P. S., Prasannamedha, G., Harshana, S., Srividya, S. J., Harrinei, K., & Indraganti, S. (2022). Analysis and prediction of water quality using deep learning and auto deep learning techniques. Science of the Total Environment, 821, 153311.

[86] Prati, L., & Richardson, Q. B. (1971). Water pollution and self-purification study on the Po River below ferrara. Water Research, 5(5), 203-212.

[87] Rahat, S. H., Steissberg, T., Chang, W., Chen, X., Mandavya, G., Tracy, J., Wasti, A., Atreya, G., Saki, S., Bhuiyan, M. A. E., & Ray, P. (2023). Remote sensing-enabled machine learning for river water quality modeling under multidimensional uncertainty. Science of the Total Environment, 898, 165504.

[88] 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.

[89] Rajkumar, H., Naik, P. K., & Rishi, M. S. (2022). A comprehensive water quality index based on analytical hierarchy process. Ecological Indicators, 145, 109582.

[90] Ramos-Ramírez, L. Á., Guevara-Luna, M. A., Chiriví-Salomón, J. S., & Muñoz-Nieto, D. M. (2020). Simulation of Cr-III dispersion in the High Bogotá River Basin using the WASP model. Revista Facultad de Ingeniería Universidad de Antioquia, 97, 30-40.

[91] Rasheed Abdul Haq, K. P., & Harigovindan, V. P. (2022). Water quality prediction for smart aquaculture using hybrid deep learning models. IEEE Access, 10, 60078-60098.

[92] Riffat, R. (2012). Fundamentals of Wastewater Treatment and Engineering. CRC Press.

[93] Sabinaya, S., Mahanty, B., Rout, P. R., Raut, S., Sahoo, S. K., Jha, V., & Sahoo, N. K. (2024). Multi-model exploration of groundwater quality and potential health risk assessment in Jajpur district, Eastern India. Environmental Geochemistry and Health, 46(2), 57.

[94] Sahoo, M. M., Patra, K. C., & Khatua, K. K. (2015). Inference of water quality index using ANFIA and PCA. Aquatic Procedia, 4, 1099-1106.

[95] Sakaa, B., Elbeltagi, A., Boudibi, S., Chaffaï, H., Islam, A. R. M. T., Kulimushi, L. C., Choudhari, P., Hani, A., Brouziyne, Y., & Wong, Y. J. (2022). Water quality index modeling using random forest and improved SMO algorithm for support vector machine in Saf-Saf river basin. Environmental Science and Pollution Research, 29(32), 48491-48508.

[96] Saravanan, S., Singh, L., Sathiyamurthi, S., Sivakumar, V., Velusamy, S., & Shanmugamoorthy, M. (2023). Predicting phosphorus and nitrate loads by using SWAT model in Vamanapuram River Basin, Kerala, India. Environmental Monitoring and Assessment, 195(1), 186.

[97] 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.

[98] Schwarzenbach, R. P., Egli, T., Hofstetter, T. B., Von Gunten, U., & Wehrli, B. (2010). Global water pollution and human health. Annual Review of Environment and Resources, 35(1), 109-136.

[99] Seifi, A., Dehghani, M., & Singh, V. P. (2020). Uncertainty analysis of water quality index (WQI) for groundwater quality evaluation: Application of Monte-Carlo method for weight allocation. Ecological Indicators, 117, 106653.

[100] Shah, K. A., & Joshi, G. S. (2017). Evaluation of water quality index for River Sabarmati, Gujarat, India. Applied Water Science, 7, 1349-1358.

[101] Sharma, D., & Kansal, A. (2013). Assessment of river quality models: A review. Reviews in Environmental Science and Bio/Technology, 12, 285-311.

[102] Sharma, S., & Reddy, A. S. (2013). Development of water quality indices for designated best uses of surface water body. International Journal of Engineering Science and Innovative Technology, 2(5), 82-93.

[103] Shil, S., Singh, U. K., & Mehta, P. (2019). Water quality assessment of a tropical river using water quality index (WQI), multivariate statistical techniques and GIS. Applied Water Science, 9, 168.

[104] Shin, S., Her, Y., Muñoz-Carpena, R., & Yu, X. (2023). Quantifying the contribution of external loadings and internal hydrodynamic processes to the water quality of Lake Okeechobee. Science of the Total Environment, 883, 163713.

[105] Shoaei, S. M., Arjmandi, S. A., & Mirbagheri, S. A. (2022). Enhancing the CE-QUAL-W2 to model dissolved lead transport and transformation in dam reservoirs: A case study of Shahid Rajaei Dam reservoir, north part of Iran. Environmental Science and Pollution Research, 29(38), 57860-57872.

[106] Smith, D. G. (1990). A better water quality indexing system for rivers and streams. Water Research, 24(10), 1237-1244.

[107] Solanki, A., Agrawal, H., & Khare, K. (2015). Predictive analysis of water quality parameters using deep learning. International Journal of Computer Applications, 125(9), 29-34.

[108] Stambuk-Giljanovic, N. (1999). Water quality evaluation by index in Dalmatia. Water Research, 33(16), 3423-3440.

[109] Streeter, H. W., & Phelps, E. B. (1925). A study of the pollution and natural purification of the Ohio River (No. 146). United States Public Health Service.

[110] Sutadian, A. D., Muttil, N., Yilmaz, A. G., & Perera, B. J. C. (2016). Development of river water quality indices—a review. Environmental Monitoring and Assessment, 188, 58.

[111] Sutadian, A. D., Muttil, N., Yilmaz, A. G., & Perera, B. J. C. (2018). Development of a water quality index for rivers in West Java Province, Indonesia. Ecological Indicators, 85, 966-982.

[112] Swaroop Bhargava, D. (1983). Use of water quality index for river classification and zoning of Ganga River. Environmental Pollution Series B, Chemical and Physical, 6(1), 51-67.

[113] Syeed, M. M. M., Hossain, M. S., Karim, M. R., Uddin, M. F., Hasan, M., & Khan, R. H. (2023). Surface water quality profiling using the water quality index, pollution index and statistical methods: A critical review. Environmental and Sustainability Indicators, 18, 100247.

[114] Talukdar, S., Shahfahad, S., Bera, S., Naikoo, M. W., Ramana, G. V., Mallik, S., Kumar, P. A., & Rahman, A. (2024). Optimisation and interpretation of machine and deep learning models for improved water quality management in Lake Loktak. Journal of Environmental Management, 351, 119866.

[115] Thi Minh Hanh, P., Sthiannopkao, S., The Ba, D., & Kim, K. W. (2011). Development of water quality indexes to identify pollutants in Vietnam's surface water. Journal of Environmental Engineering, 137(4), 273-283.

[116] Thomas Jr., H. A. (1949). Pollution load capacity of streams. Water & Sewage Works, 96(7), 264-266.

[117] Tiwari, A. K., De Maio, M., Singh, P. K., & Mahato, M. K. (2015). Evaluation of surface water quality by using GIS and a heavy metal pollution index (HPI) model in a coal mining area, India. Bulletin of Environmental Contamination and Toxicology, 95, 304-310.

[118] Tiwari, S., Babbar, R., & Kaur, G. (2018). Performance evaluation of two ANFIS models for predicting water quality index of River Satluj (India). Advances in Civil Engineering, 2018(1), 8971079.

[119] Tiyasha, T. M., Tung, T. M., & Yaseen, Z. M. (2020). A survey on river water quality modelling using artificial intelligence models: 2000–2020. Journal of Hydrology, 585, 124670.

[120] Tripathi, M., & Singal, S. K. (2019). Use of principal component analysis for parameter selection for development of a novel water quality index: A case study of river Ganga India. Ecological Indicators, 96, 430-436.

[121] Uddin, M. G., Jackson, A., Nash, S., Rahman, A., & Olbert, A. I. (2023). Comparison between the WFD approaches and newly developed water quality model for monitoring transitional and coastal water quality in Northern Ireland. Science of the Total Environment, 901, 165960.

[122] Uddin, M. G., Nash, S., Mahammad Diganta, M. T., Rahman, A., & Olbert, A. I. (2022). Robust machine learning algorithms for predicting coastal water quality index. Journal of Environmental Management, 321, 115923.

[123] Uddin, M. G., Nash, S., & Olbert, A. I. (2021). A review of water quality index models and their use for assessing surface water quality. Ecological Indicators, 122, 107218.

[124] Uddin, M. G., Nash, S., Rahman, A., & Olbert, A. I. (2023). A sophisticated model for rating water quality. Science of the Total Environment, 868, 161614.

[125] Uddin, M. G., Rahman, A., Nash, S., Diganta, M. T. M., Sajib, A. M., Moniruzzaman, M., & Olbert, A. I. (2023). Marine waters assessment using improved water quality model incorporating machine learning approaches. Journal of Environmental Management, 344, 118368.

[126] Ustaoğlu, F., Tepe, Y., & Taş, B. (2020). Assessment of stream quality and health risk in a subtropical Turkey river system: A combined approach using statistical analysis and water quality index. Ecological Indicators, 113, 105815.

[127] Verma, M., Loganathan, V. A., & Bhatt, V. K. (2022). Development of entropy and deviation-based water quality index: Case of River Ganga, India. Ecological Indicators, 143, 109319.

[128] Wan, H., Xu, R., Zhang, M., Cai, Y., Li, J., & Shen, X. (2022). A novel model for water quality prediction caused by non-point sources pollution based on deep learning and feature extraction methods. Journal of Hydrology, 612, 128081.

[129] Wang, Q., Li, S., Jia, P., Qi, C., & Ding, F. (2013). A review of surface water quality models. The Scientific World Journal, 2013(1), 231768.

[130] Welander, P. (1968). Wind-driven circulation in one-and two-layer oceans of variable depth. Tellus, 20(1), 1-16.

[131] Wepener, V., Euler, N., Van Vuren, J. H. J., Du Preez, H. H., & Kohler, A. (1992). The development of an aquatic toxicity index as a tool in the operational management of water quality in the Olifants River (Knsger National Park). Koedoe, 35(2), a400.

[132] Woo, S. Y., Kim, S. J., Lee, J. W., Kim, S. H., & Kim, Y. W. (2021). Evaluating the impact of interbasin water transfer on water quality in the recipient river basin with SWAT. Science of the Total Environment, 776, 145984.

[133] Wool, T. A., Ambrose, R. B., Martin, J. L., Comer, E. A., & Tech, T. (2006). Water quality analysis simulation program (WASP). User's Manual. Version, 6. US EPA.

[134] Wu, Z., Lai, X., & Li, K. (2021). Water quality assessment of rivers in Lake Chaohu Basin (China) using water quality index. Ecological Indicators, 121, 107021.

[135] Xu, S., He, G., Fang, H., Bai, S., & Wu, X. (2022). Parameter uncertainty and sensitivity analysis of the three Gorges Reservoir and Xiangxi River EFDC model. Journal of Hydrology, 610, 127881.

[136] Yih, S. M., & Davidson, B. (1975). Identification in nonlinear, distributed parameter water quality models. Water Resources Research, 11(5), 693-704.

[137] 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.

[138] Yu, Z., Dai, H., Yang, J., Zhu, Y., & Yuan, S. (2024). Global sensitivity analysis with deep learning-based surrogate models for unraveling key parameters and processes governing redox zonation in Riparian zone. Journal of Hydrology, 638, 131442.

[139] Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338-353.

[140] Zamani, M. G., Nikoo, M. R., Jahanshahi, S., Barzegar, R., & Meydani, A. (2023). Forecasting water quality variable using deep learning and weighted averaging ensemble models. Environmental Science and Pollution Research, 30(59), 124316-124340.

[141] Zeiger, S. J., & Hubbart, J. A. (2016). A SWAT model validation of nested-scale contemporaneous stream flow, suspended sediment and nutrients from a multiple-land-use watershed of the central USA. Science of the Total Environment, 572, 232-243.

[142] Zeinalzadeh, K., & Rezaei, E. (2017). Determining spatial and temporal changes of surface water quality using principal component analysis. Journal of Hydrology: Regional Studies, 13, 1-10.

[143] Zhang, L., Cui, J., Song, T., & Liu, Y. (2018). Application of an AQUATOX model for direct toxic effects and indirect ecological effects assessment of polycyclic aromatic hydrocarbons (PAHs) in a plateau eutrophication lake, China. Ecological Modelling, 388, 31-44.

[144] Zhang, L., & Liu, J. (2014). AQUATOX coupled foodweb model for ecosystem risk assessment of polybrominated diphenyl ethers (PBDEs) in lake ecosystems. Environmental Pollution, 191, 80-92.

[145] Zhang, P., Liu, R., Bao, Y., Wang, J., Yu, W., & Shen, Z. (2014). Uncertainty of SWAT model At different DEM resolutions In a large mountainous watershed. Water Research, 53, 132-144.

[146] Zhang, Y., Xia, J., Shao, Q., & Zhai, X. (2013). Water quantity and quality simulation by improved SWAT in highly regulated Huai River Basin of China. Stochastic Environmental Research and Risk Assessment, 27, 11-27.

[147] Zhang, Z., Sun, B., & Johnson, B. E. (2015). Integration of a benthic sediment diagenesis module into the two dimensional hydrodynamic and water quality model–CE-QUAL-W2. Ecological Modelling, 297, 213-231.

[148] Zheng, H., Liu, Y., Wan, W., Zhao, J., & Xie, G. (2023). Large-scale prediction of stream water quality using an interpretable deep learning approach. Journal of Environmental Management, 331, 117309.

[149] Zhi, W., Feng, D., Tsai, W. P., Sterle, G., Harpold, A., Shen, C., & Li, L. (2021). From hydrometeorology to river water quality: Can a deep learning model predict dissolved oxygen at the continental scale? Environmental Science & Technology, 55(4), 2357-2368.

[150] Zhi, W., Ouyang, W., Shen, C., & Li, L. (2023). Temperature outweighs light and flow as the predominant driver of dissolved oxygen in US Rivers. Nature Water, 1(3), 249-260.

[151] Zhou, Y. (2020). Real-time probabilistic forecasting of river water quality under data missing situation: Deep learning plus post-processing techniques. Journal of Hydrology, 589, 125164.

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