Abba, S. I., Pham, Q. B., Saini, G., Linh, N. T. T., Ahmed, A. N., Mohajane, M., ... & Bach, Q. V. (2020). Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index. Environmental Science and Pollution Research, 27, 41524-41539. https://doi.org/10.1007/s11356-020-09689-x
Ahmed, M., Mumtaz, R., & Hassan Zaidi, S. M. (2021). Analysis of water quality indices and machine learning techniques for rating water pollution: A case study of Rawal Dam, Pakistan. Water Supply, 21(6), 3225-3250. https://doi.org/10.2166/ws.2021.082
Asadollah, S. B. H. S., Sharafati, A., Motta, D., & Yaseen, Z. M. (2021). River water quality index prediction and uncertainty analysis: A comparative study of machine learning models. Journal of Environmental Chemical Engineering, 9(1), 104599. https://doi.org/10.1016/j.jece.2020.104599
Awan, S., Ippolito, J. A., Ullman, J., Ansari, K., Cui, L., & Siyal, A. (2021). Biochars reduce irrigation water sodium adsorption ratio. Biochar, 3, 77-87. https://doi.org/10.1007/s42773-020-00073-z
Azma, A., Liu, Y., Azma, M., Saadat, M., Zhang, D., Cho, J., & Rezania, S. (2023). Hybrid machine learning models for prediction of daily dissolved oxygen. Journal of Water Process Engineering, 54, 103957. https://doi.org/10.1016/j.jwpe.2023.103957
Darwiche-Criado, N., Jiménez, J. J., Comín, F. A., Sorando, R., & Sánchez-Pérez, J. M. (2015). Identifying spatial and seasonal patterns of river water quality in a semiarid irrigated agricultural Mediterranean basin. Environmental Science and Pollution Research, 22, 18626-18636. https://doi.org/10.1007/s11356-015-5484-5
Divband Hafshejani, L., Naseri, A. A., Moradzadeh, M., Daneshvar, E., & Bhatnagar, A. (2022). Applications of soft computing techniques for prediction of pollutant removal by environmentally friendly adsorbents (case study: the nitrate adsorption on modified hydrochar). Water Science & Technology, 86(5), 1066-1082. https://doi.org/10.2166/wst.2022.264
Egbueri, J. C., Ameh, P. D., & Unigwe, C. O. (2020). Integrating entropy-weighted water quality index and multiple pollution indices towards a better understanding of drinking water quality in Ojoto area, SE Nigeria. Scientific African, 10, e00644. https://doi.org/10.1016/j.sciaf.2020.e00644
Ejaz, U., Khan, S. M., Jehangir, S., Ahmad, Z., Abdullah, A., Iqbal, M., ... & Svenning, J. C. (2024). Monitoring the Industrial waste polluted stream-Integrated analytics and machine learning for water quality index assessment. Journal of Cleaner Production, 450, 141877. https://doi.org/10.1016/j.jclepro.2024.141877
El Behairy, R. A., El Baroudy, A. A., Ibrahim, M. M., Kheir, A. M., & Shokr, M. S. (2021). Modelling and assessment of irrigation water quality index using GIS in semi-arid region for sustainable agriculture. Water, Air, & Soil Pollution, 232(9), 352. https://doi.org/10.1007/s11270-021-05310-0
El Bilali, A., & Taleb, A. (2020). Prediction of irrigation water quality parameters using machine learning models in a semi-arid environment. Journal of the Saudi Society of Agricultural Sciences, 19(7), 439-451. https://doi.org/10.1016/j.jssas.2020.08.001
Fahimi, F., Yaseen, Z. M., & El-shafie, A. (2017). Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review. Theoretical and applied climatology, 128, 875-903. https://doi.org/10.1007/s00704-016-1735-8
Haile, D., & Gabbiye, N. (2022). The applications of Canadian water quality index for ground and surface water quality assessments of Chilanchil Abay watershed: The case of Bahir Dar city waste disposal site. Water Supply, 22(1), 89-109. https://doi.org/10.2166/ws.2021.286
Isaac, R., Khura, T., & Wurmbrand, J. (2009). Surface and subsurface water quality appraisal for irrigation. Environmental monitoring and assessment, 159, 465-473. https://doi.org/10.1007/s10661-008-0643-5
Jaloree, S., Rajput, A., & Gour, S. (2014). Decision tree approach to build a model for water quality. Binary Journal of Data Mining & Networking, 4(1), 25-28. https://doi.org/10.5138/BJDMN.V4I1.1563
Jha, M. K., Shekhar, A., & Jenifer, M. A. (2020). Assessing groundwater quality for drinking water supply using hybrid fuzzy-GIS-based water quality index. Water Research, 179, 115867. https://doi.org/10.1016/j.watres.2020.115867
Kadam, A., Wagh, V., Muley, A., Umrikar, B., & Sankhua, R. (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. https://doi.org/10.1007/s40808-019-00581-3
Kouadri, S., Elbeltagi, A., Islam, A. R. M. T., & Kateb, S. (2021). Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast). Applied Water Science, 11(12), 190. https://doi.org/10.1007/s13201-021-01528-9
Li, X., Ding, J., & Ilyas, N. (2021). Machine learning method for quick identification of water quality index (WQI) based on Sentinel-2 MSI data: Ebinur Lake case study. Water Supply, 21(3), 1291-1312. https://doi.org/10.2166/ws.2020.381
Lv, L., Wang, J., Li, J., Zhang, B., & Gao, S. (2023). A Hybrid Model Based on LSTM for Water Prediction Algorithm. 2023 6th International Symposium on Autonomous Systems (ISAS). https://doi.org/10.1109/ISAS59543.2023.10164338
Maroju, R. G., Choudhari, S. G., Shaikh, M. K., Borkar, S. K., & Mendhe, H. (2023). Application of Artificial Intelligence in the Management of Drinking Water: A Narrative Review. Cureus, 15(11). https://doi.org/10.7759/cureus.49344
Meireles, A. C. M., Andrade, E. M. D., Chaves, L. C. G., Frischkorn, H., & Crisostomo, L. A. (2010). A new proposal of the classification of irrigation water. Revista Ciência Agronômica, 41, 349-357. https://doi.org/10.1590/S1806-66902010000300005
Mohammadpour, R., Shaharuddin, S., Chang, C. K., Zakaria, N. A., Ghani, A. A., & Chan, N. W. (2015). Prediction of water quality index in constructed wetlands using support vector machine. Environmental Science and Pollution Research, 22, 6208-6219. https://doi.org/10.1007/s11356-014-3806-7
Mohseni, U., Pande, C. B., Pal, S. C., & Alshehri, F. (2024). Prediction of weighted arithmetic water quality index for urban water quality using ensemble machine learning model. Chemosphere, 141393. https://doi.org/10.1016/j.chemosphere.2024.141393
Raheja, H., Goel, A., & Pal, M. (2022). Prediction of groundwater quality indices using machine learning algorithms. Water Practice & Technology, 17(1), 336-351. https://doi.org/10.2166/wpt.2021.120
Rajab, K., & Esmail, A. (2023). Role of ion pairs and activity in estimation of ionic strength from electrical conductivity of irrigation water. Iraqi Journal of Agricultural Sciences, 54(3), 755-767. https://doi.org/10.36103/ijas.v54i3.1758
Serder, M., Islam, M., Hasan, M., Yeasmin, M., & Mostafa, M. (2020). Assessment of coastal surface water quality for irrigation purpose. Water Practice & Technology, 15(4), 960-972. https://doi.org/10.2166/wpt.2020.070
Singh, G., Wani, O. A., Egbueri, J. C., Salaria, A., & Singh, H. (2023). Seasonal variation of the quality of groundwater resources for human consumption and industrial purposes in the central plain zone of Punjab, India. Environmental Monitoring and Assessment, 195(12), 1454. https://doi.org/10.21203/rs.3.rs-2800041/v1
Singha, S., Pasupuleti, S., Singha, S. S., Singh, R., & Kumar, S. (2021). Prediction of groundwater quality using efficient machine learning technique. Chemosphere, 276, 130265. https://doi.org/10.1016/j.chemosphere.2021.130265
Subiantoro, R. (2022). Assessment of Water Quality for Agricultural Cultivation Irrigation Using the Irrigation Water Quality Index: A Case-Study Land Survey and Evaluation from Kampus Polinela II. IOP Conference Series: Earth and Environmental Science. https://doi.org/10.1088/1755-1315/1012/1/012049
Sun, Y., Chen, X., Luo, Y., Cao, D., Feng, H., Zhang, X., & Yao, R. (2023). Agricultural Water Quality Assessment and Application in the Yellow River Delta. Agronomy, 13(6), 1495. https://doi.org/10.3390/agronomy13061495
Tas, I., Yildirim, Y. E., & Gokalp, Z. (2022). The effect of excessive sodium-containing irrigation waters on soil infiltration rate. Current Trends in Natural Sciences, 11(22), 19-28. https://doi.org/10.47068/ctns.2022.v11i22.002
Trach, R., Trach, Y., Kiersnowska, A., Markiewicz, A., Lendo-Siwicka, M., & Rusakov, K. (2022). A study of assessment and prediction of water quality index using fuzzy logic and ANN models. Sustainability, 14(9), 5656. https://doi.org/10.3390/su14095656
Wang, X., Li, Y., Qiao, Q., Tavares, A., & Liang, Y. (2023). Water quality prediction based on machine learning and comprehensive weighting methods. Entropy, 25(8), 1186. https://doi.org/10.3390/e25081186
Yıldız, S., & Karakuş, C. B. (2020). Estimation of irrigation water quality index with development of an optimum model: a case study. Environment, Development and Sustainability, 22, 4771-4786. https://doi.org/10.1007/s10668-019-00405-5
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. https://doi.org/10.1016/j.envpol.2022.119136
Zhao, X., Wang, H., Tang, Z., Zhao, T., Qin, N., Li, H., ... & Giesy, J. P. (2018). Amendment of water quality standards in China: viewpoint on strategic considerations. Environmental Science and Pollution Research, 25, 3078-3092. https://doi.org/10.1007/s11356-016-7357-y
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