Water Leakage Localization for Smart Water Mgmt Using ML Techniques
Presented By: G Shobha
In this session, Machine Learning Models for smart water management system which automates the identification of leakage and also predict the location of leakages in the water pipeline will be delivered.
Attendees will be able to learn different ML techniques used to predict the leakage in the water pipes and also understand the best approach that can be used to localize the water leakage. The system determines leakages by utilizing the water flow rate in the water pipes The session also highlights the prototype that was developed using STAR-CCM+, a computational fluid dynamics software to test the proposed system.
Machine Learning models were tested on the prototype developed. The results showed that amongst the machine learning based location prediction models, the Multi-Layer Perceptron (MLP) performs the best with an accuracy of 94.47% and an F1 score of 0.95.
Who Should Attend?
- Undergraduate, graduate students, Research Scholars who are interested or working in the smart cities domain / Artificial Intelligence / Machine Learning
About the Speaker
Shobha G, Professor, Computer Science and Engineering Department, RV College of Engineering, Bengaluru, India have teaching experience of 26 years, her specialization includes Data mining, Machine Learning and Image processing. She has published more than 150 papers in reputed journals / conferences. She has also executed sponsored projects worth INR 200 lakhs funded from various agencies nationally and internationally. She is a recipient of various awards such as Career Award for young teachers 2007-08 constituted by All India Council of Technical Education, Best Researcher award from Cognizant 2017, GHC Faculty Scholar for Women in Computing in 2018, IBM Shared University Research Award in 2019, HPCC Systems community recognition award 2020. She is also an advisory committee member for IET India Scholarship Award 2021.
Tags & Topics for This Webinar:
- Leakage detection, Location prediction, STAR-CCM+, Machine Learning (Ml), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM)