Journal Press India®

Water Quality Monitoring on Streaming Data

Vol 3 , Issue 2 , July - December 2023 | Pages: 98-113 | Research Paper  

https://doi.org/10.17492/computology.v3i2.2305


Author Details ( * ) denotes Corresponding author

1. Bhawnesh Kumar, Assistant Professor, Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India (bhawneshmca@gmail.com)
2. * Tinku Singh, Postdoctoral Researcher, School of Information and Communication Engineering, Chungbuk National University, Cheongju-si, Chungcheongbuk-do, Korea, Republic of (tinku.singh@cbnu.ac.kr)
3. Anuj Kumar, Assistant Professor, Department of Computer Science and Engineering, Graphic Era Hill University, Dehradun, Uttarakhand, India (anujkumardixit@gehu.ac.in)
4. Naveen Kumar, Assistant Professor, School of Computing, DIT University, Dehradun, Uttarakhand, India (naveen.kumar@dituniversity.edu.in)

The increasing contamination of natural water bodies due to diverse human activities necessitates a comprehensive approach to monitoring water quality, especially considering its widespread use in daily life. This study addresses the escalating contamination of natural water bodies, emphasizing the need for a robust real-time water quality monitoring system. Focused on evaluating Triveni Sangam, Prayagraj, where the Ganga and Yamuna rivers converge, the study recognizes the crucial role of continuous monitoring in safeguarding precious water resources. To achieve this, a sophisticated framework has been proposed, leveraging a Spark server to simulate streaming data. This dynamic approach ensures uninterrupted and real-time assessment of water quality, crucial for the effective management of water resources. The system categorizes training data using the Water Quality Index (WQI) and employs Naive Bayes classification for real-time data, achieving an impressive accuracy of 82.21%. The results underscore the effectiveness of learning from streaming data, emphasizing its utility for monitoring water quality in real-time. This study contributes significantly to ongoing water resource management initiatives but also highlights the pivotal role of machine learning in addressing pressing environmental challenges.

Keywords

Streaming Data; WQI; Real-Time Monitoring; Classification; Incremental Learning

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