Akshat Choudhary, Dr Manjot Kaur, Dr Gurpreet Singh, Dr Chaitanya Singla | International Journal of Microelectronics and Digital integrated circuits | Vol 11, Issue 01 | ISSN: 2456-3986
Abstract
Quality of drinking water is one of the critical determinants of public health, leading to severe environmental and health-related issues when poor. The paper examines the integration of machine learning techniques that could improve accuracy and efficiency in assessing drinking water quality. The traditional techniques of monitoring drinking water are resource-intensive yet lack timely insight into contamination events. In this scenario, an evaluation of the performance of ML models such as Random Forest, SVM, and Neural Networks is conducted to analyze comprehensive datasets generated from water sensors. The methodology followed includes data collection, preprocessing, feature extraction, and training model, resulting in real-time predictions of water potability. Findings have shown that ML approaches outperform traditional statistical methods to look forward to effective management of the environment and to lower health risks from water pollution. This research indicates not only the potential of ML in the automated prediction of water quality but also the requirement for better monitoring strategies while securing safe water as new complications from climate change and pollution continue to deteriorate.
Keywords:Machine Learning (ML), Water Potability Prediction, Feature Engineering, Support Vector Machines (SVM), Water Quality Index (WQI), Neural Networks, Predictive Modeling.
Keywords
Neural networks, machine learning (ML), Water Potability Prediction, Water Quality Index (WQI), Feature Engineering, Support Vector Machines (SVM)
References
[1] Miller T, Durlik I, Kostecka E, Kozlovska P, Łobodzińska A, Sokołowska S, Nowy A. Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data. Electronics. 2025 Feb 11;14(4):696. [2] Bownik A, Wlodkowic D. Advances in real-time monitoring of water quality using automated analysis of animal behaviour. Science of the Total Environment. 2021 Oct 1; 789:147796. [3] Selvakumar P, Srivastava RR, Bhattacharya S, Das A, Manjunath TC, Gupta S. AI Applications in Drinking-Water Management. InSmart Water Technology for Sustainable Management in Modern Cities 2025 (pp. 307-336). IGI Global Scientific Publishing. [4] Essamlali I, Nhaila H, El Khaili M. Advances in machine learning and IoT for water quality monitoring: A comprehensive review. Heliyon. 2024 Mar 13. [5] Zhu M, Wang J, Yang X, Zhang Y, Zhang L, Ren H, Wu B, Ye L. A review of the application of machine learning in water quality evaluation. Eco-Environment & Health. 2022 Jun 1;1(2):107-16. [6] Ighalo JO, Adeniyi AG, Marques G. Artificial intelligence for surface water quality monitoring and assessment: a systematic literature analysis. Modeling Earth Systems and Environment. 2021 Jun;7(2):669-81. [7] World Health Organization, United Nations Children's Fund. Water, sanitation, hygiene, waste and electricity services in health care facilities: progress on the fundamentals. 2023 global report. World Health Organization; 2023 Jun 13. [8] Srikanth R. Challenges of sustainable water quality management in rural India. Current Science. 2009 Aug 10:317-25. [9] Kumar A, Juyal R, Bisht M, Kumar S, Patel SK. Causes, effects and solutions of groundwater pollution. Krishi science. 2022;3(3). [10] Lal R. Restoring soil and water resources and mitigating climate change in India by judicious management of agricultural and urban wastes. Journal of the Indian Society of Soil Science. 2017;65(2):105-17. [11] Sharma A, Sharma R, Rana R, Kalia A. Water quality prediction using Machine Learning Models. InE3S Web of Conferences 2024 (Vol. 596, p. 01025). EDP Sciences. [12] Kaddoura S. Evaluation of machine learning algorithm on drinking water quality for better sustainability. Sustainability. 2022 Sep 13;14(18):11478. [13] Ghosh H, Tusher MA, Rahat IS, Khasim S, Mohanty SN. Water quality assessment through predictive machine learning. InInternational Conference on Intelligent Computing and Networking 2023 Feb 24 (pp. 77-88). Singapore: Springer Nature Singapore. [14] Aldhyani TH, Alrasheedi M, Alqarni AA, Alzahrani MY, Bamhdi AM. Intelligent hybrid model to enhance time series models for predicting network traffic. IEEE Access. 2020 Jul 14;8:130431-51. [15] Al-Othman AA. Evaluation of the suitability of surface water from Riyadh Mainstream Saudi Arabia for a variety of uses. Arabian Journal of Chemistry. 2019 Dec 1;12(8):2104-10. [16] Tyagi S, Sharma B, Singh P, Dobhal R. Water quality assessment in terms of water quality index. American Journal of water resources. 2013 Jan;1(3):34-8. [17] A. A. Al-Othman, “Evaluation of the suitability of surface water from riyadh mainstream saudi arabia for a variety of uses,” Arabian Journal of Chemistry, vol. 12, no. 8, pp. 2104–2110, 2019. [18] Al-Othman AA. Evaluation of the suitability of surface water from Riyadh Mainstream Saudi Arabia for a variety of uses. Arabian Journal of Chemistry. 2019 Dec 1;12(8):2104-10. [19] Aghlmand R, Nezami SR. Evaluation of drinking water quality using WQI: A case study in Ardabil Province, Iran. In12th International Congress on Civil Engineering 2021 (pp. 1-4).
