Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{196901,
        author = {D.RADHIKA and PAVITHRA V and RAKSHA R and ROHINI T},
        title = {IOT-Enabled Advanced Water Quality System for River Management},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {5706-5711},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196901},
        abstract = {Water Pollution in rivers has become a serious environmental and public health concern due to rapid industrialization, urbanization, and improper waste disposal. Continuous monitoring of water quality is essential to ensure safe and sustainable water resources. This paper presents the design and development of an IOT-enabled advanced water quality monitoring system for effective river management. The proposed system utilizes multiple sensors to measure important water parameters such as pH, turbidity, temperature, dissolved oxygen, and salinity. These sensors are interfaced with a microcontroller to collect real-time data, which is then processed and transmitted to a cloud-based platform using IOT communication technologies. To enhance decision-making, a machine learning approach based on Support Vector Machine (SVM) algorithm is used to categorize water quality conditions as safe or unsafe in order to improve decision-making. Techniques for feature extraction and data preprocessing are used to increase system dependability and prediction accuracy. Additionally, the system produces automatic alerts when surpass allowable limits and has a web-based interface for real-time visualization of water quality data. Continuous monitoring, early pollution detection, and remote access to water quality data are all made possible by this integrated solution. The suggested system provides an economical, scalable and effective method for managing river water, assisting authorities in taking prompt action to safeguard public health and environmental resources.},
        keywords = {Internet of Things (IOT), Water Quality, River Management, Sensors, Machine Learning, Support Vector Machine (SVM), Real Time Monitoring, Cloud Computing.},
        month = {April},
        }