data analytics and machine learning for water quality forecasting
JayaSuriya P,Sethupathi V,Suresh D
Published in International Journal of Advanced Research in Computer Networking,Wireless and Mobile Communications
ISSN: 2320-7248 Impact Factor:1.8 Volume:5 Issue:3 Year: 25 April,2025 Pages:290-295
Abstract
Water quality forecasting plays a significant role in managing environmental resources and safeguarding public health. In this paper, a data-driven approach is proposed to forecast water quality parameters using advanced data analytics and machine learning techniques. Policies in this context refer to algorithmic rules governing the system's predictive behaviour to ensure consistent, accurate, and reliable outcomes. The methodology involves gathering and processing historical water quality records alongside meteorological and environmental data to build an adaptive forecasting model. The system identifies anomalies, patterns, and correlations using various regression and classification algorithms. Multiple stakeholders such as environmental monitoring agencies, public health departments, and data scientists form an interconnected structure responsible for data supervision and model updates. Based on their roles, access privileges determine who can update or validate predictions. The data preprocessing initiates the model training process, where secure data handling ensures model integrity. Advanced analytics tools help in selecting relevant features and optimizing performance. Finally, the integration of these models provides a reliable forecast system for identifying potential contamination, thereby enabling timely action and sustainable water resource management.
Kewords
Data Analytics, Machine Learning, Water Quality Forecasting.
Reference
[1]. O. Ajayi, A. B. Bagula, H. C. Maluleke, Z. Gaffoor, N. Jovanovic and K. C. Pietersen, "WaterNet: A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes," in IEEE Access, vol. 10, pp. 48318- 48337, 2022, doi: 10.1109/ACCESS.2022.3172274. [2]. N. A. P. Rostam, N. H. A. H. Malim, R. Abdullah, A. L. Ahmad, B. S. Ooi, and D. ssssJ. C. Chan, “A complete proposed framework for coastal water quality monitoring system with algae predictive model,” IEEE Access, vol. 9, pp. 108 249–108 265, 2021. [3]. W. Liu et al., "A Novel Hybrid Model to Predict Dissolved Oxygen for Efficient Water Quality in Intensive Aquaculture," in IEEE Access, vol. 11, pp. 29162-29174, 2023, doi: 10.1109/ACCESS.2023.3260089. [4]. O Al-Sulttani, M. Al-Mukhtar, A. B. Roomi, A.A. Farooque, K. M. Khedher and Z. M. Yaseen, "Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction," in IEEE Access, vol. 9, pp. 108527- 108541, 2021, doi: 10.1109/ACCESS.2021.3100490. [5]. S. R. S. Chakravarthy et al., "Prediction of Water Quality Using SoftMax-ELM Optimized Using Adaptive CrowSearch Algorithm," in IEEE Access, vol. 11, pp. 140900- 140913, 2023, doi: 10.1109/ACCESS.2023.3339564. [6]. Omambia, B. Maake, and A. Wambua, “Water quality monitoring using IoT & machine learning,” in 2022 ISTAfrica Conference (IST- Africa), 2022, pp. 1–8 [7]. M. G. Uddin, S. Nash, and A. I. Olbert, “A review of water quality index models and their use for assessing surface water quality,” Ecology. Indicators, vol. 122, Mar. 2021, Art. no. 107218. [8]. E. Olmedo and M. Smith, “Development of an IoT water quality monitoring system for data-based aquaculture siting,” in Southeast Con 2021, 2021, pp. 1–8. [9]. J. I. Ubah, L. C. Orakwe, K. N. Ogbu, J. I. Awu, I. E. Ahaneku, and E. C. Chukwuma, ‘‘Forecasting water quality parameters using artificial neural network for irrigation purposes,’’ Sci. Rep., vol. 11, no. 1, Dec. 2021, Art. no. 24438 [10]. M. Najafzadeh and S. Basirian, ‘‘Evaluation of river water quality index using remote sensing and artificial intelligence models,’’ Remote Sens., vol. 15, no. 9, p. 2359, Apr. 2023
