-
Members: Free
IEEE Members: $11.00
Non-members: $15.00Length: 00:37:17
29 Mar 2022
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.
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.
