An efficient scheme for water leakage detection using support vector machines (SVM)-Zig
- Title
- An efficient scheme for water leakage detection using support vector machines (SVM)-Zig
- Creator
- Kothari A.; Balamurugan M.
- Description
- Water is one of the most essential and valuable resources for all living beings, yet in the present day, there is a scarcity of it. Half of the water loss in large cities and industries is due to leaks and illegal lines. 10%-20% of water loss can be reduced by detecting leaks but without the presence of advanced monitoring systems, this problem is typically worsened. Monitoring the consumption and leak detection for such large areas is a challenging task. To overcome this issue a small prototype is prepared called Zig. Zig is designed for both household and industrial purposes. Its main aim is to monitor the flow and consumption of water at different levels of a building like a first-floor and so on which may represent some industrial and household situation. This work focuses on pressure/flow monitoring method to reduce the operational cost and also to detect leakage. One of the machine learning algorithms, Support Vector Machines (SVM) has been applied to detect the leakage and it is compared with Random Forest algorithm to show that proposed scheme is detecting water leakage better. BEIESP.
- Source
- International Journal of Recent Technology and Engineering, Vol-7, No. 6, pp. 39-46.
- Date
- 2019-01-01
- Publisher
- Blue Eyes Intelligence Engineering and Sciences Publication
- Subject
- Machine Learning; Random Forest; Support Vector Machines; Water Leakage Detection
- Coverage
- Kothari A., Department of CSE, Christ (Deemed to be University), Bengaluru, Karnataka, India; Balamurugan M., Department of CSE, Christ (Deemed to be University), Bengaluru, Karnataka, India
- Rights
- Restricted Access
- Relation
- ISSN: 22773878
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Kothari A.; Balamurugan M., “An efficient scheme for water leakage detection using support vector machines (SVM)-Zig,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/16689.