Abstract
Water in daily life is an imperative and needful resource to human beings and all other living organisms. Water contamination by adding pollutants leads to noxious effects and is the new way of introducing disease to people. Thus, machine learning plays a vital role in Predicting whether the water is in excellent or imperfect condition. The machine learning algorithm is used to predict the water quality, and statistical accuracy parameters are calculated for each machine learning algorithm. SVM, KNN, Decision Tree classifiers, XG Boost, Ada boost and Feed Forward Neural Networks are used to predict water conditions. Compared to Conventional Machine learning algorithms, Feed Forward Neural network predicts the water quality with 98% accuracy. Precision values and more statistical parameters are measured and compared by evaluating the accuracy values.
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References
Aldhyani, T.H., Al-Yaari, M., Alkahtani, H., Maashi, M.: Water quality prediction using artificial intelligence algorithms. Appl. Bionics Biomech. 2020 (2020)
Lerios, J.L., Villarica, M.V.: Pattern extraction of water quality prediction using machine learning algorithms of water reservoirs. Int. J. Mech. Eng. Robot. Res. 8(6), 992–997 (2019)
Haghiabi, A.H., Nasrolahi, A.H., Parsaie, A.: Water quality prediction using machine learning methods. Water Qual. Res. J. 53(1), 3–13 (2018)
Hayder, G., Kurniawan, I., Mustafa, H.M.: Implementing machine learning methods for monitoring and predicting water quality parameters. Biointerface Res. Appl. Chem 11, 9285–9295 (2020)
Lu, H., Ma, X.: Hybrid decision tree-based machine learning models for short-term water quality prediction. Chemosphere 249, 126169 (2020)
Ahmed, U., Mumtaz, R., Anwar, H., Shah, A.A., Irfan, R., García-Nieto, J.: Efficient water quality prediction using supervised machine learning. Water 11(11), 2210 (2019). https://doi.org/10.3390/w11112210
Ahmed, A.N., et al.: Machine learning methods for better water quality prediction. J. Hydrol. 578, 124084 (2019). https://doi.org/10.1016/j.jhydrol.2019.124084
Fu, Z.: Water quality prediction based on machine learning techniques. Dissertation. University of Nevada, Las Vegas (2020)
Shaikh, S.S., Shahapurkar, R.: Machine learning based quality prediction of greywater: a review. In: Information and Communication Technology for Competitive Strategies (ICTCS 2020), pp. 337–347 (2021)
Wu, J., Wang, Z.: A hybrid model for water quality prediction based on an artificial neural network, wavelet transform, and long short-term memory. Water 14(4), 610 (2022)
Ortiz-Lopez, C., Bouchard, C., Rodriguez, M.: Machine learning models with potential application to predicting source water quality for treatment purposes: a critical review. Environ. Technol. Rev. 11(1), 118–147 (2022)
Juna, A., et al.: Water quality prediction using KNN imputer and multilayer perceptron. Water 14(17), 2592 (2022)
Ambade, B., Sethi, S.S., Giri, B., Biswas, J.K., Bauddh, K.: Characterization, behavior, and risk assessment of polycyclic aromatic hydrocarbons (PAHs) in the estuary sediments. Bull. Environ. Contam. Toxicol.Contam. Toxicol. 108, 243–252 (2022)
PCRWR. National Water Quality Monitoring Programme, Fifth Monitoring Report (2005–2006); Pakistan Council of Research in Water Resources Islamabad: Islamabad, Pakistan (2007)
Najwa Mohd Rizal, N., et al.: Comparison between regression models, Support Vector Machine (SVM), and Artificial Neural Network (ANN) in river water quality prediction. Processes 10(8) 1652 (2022)
Wu, H., et al.: Water quality prediction based on multi-task learning. Int. J. Environ. Res. Public Health 19(15), 9699 (2022)
Hmoud Al-Adhaileh, M., Alsaade, F.W.: Modelling and prediction of water quality by using artificial intelligence. Sustainability 13(8), 4259 (2021)
Burba, F., Ferraty, F., Vieu, P.: K-nearest neighbor method in functional non-parametric regression. J. Nonparametr. Stat. 21, 453–469 (2009)
Geetha, A., Nasira, G.M.: Data mining for meteorological applications: decision trees for modeling rainfall prediction. In: Proceedings of the 2014 IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore, India, 18–20 December 2014, pp. 1–4. IEEE, New York (2014)
Fan, Y., Lu, W.X., Miao, T.S., An, Y., Li, J., Luo, J.: Optimal design of groundwater pollution monitoring network based on the SVR surrogate model under uncertainty. Environ. Sci. Pollut. Res. Int.Pollut. Res. Int. 27, 24090–24102 (2020)
Islam, N., Irshad, K.: Artificial ecosystem optimization with deep learning enabled water quality prediction and classification model. Chemosphere 309, 136615 (2022)
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This publication is done with the funding support of Muscat College, Muscat, Sultanate of Oman.
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Krishnan, R. et al. (2024). Prediction and Analysis of Water Quality Using Machine Learning Techniques. In: García Márquez, F.P., Jamil, A., Hameed, A.A., Segovia Ramírez, I. (eds) Emerging Trends and Applications in Artificial Intelligence. ICETAI 2023. Lecture Notes in Networks and Systems, vol 960. Springer, Cham. https://doi.org/10.1007/978-3-031-56728-5_13
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