부산대학교 도서관

This study presents an Internet of Things (IoT)-based system that utilises machine learning (ML) techniques to estimate water flow through pipes based on pressure. The system incorporates an ESP-32 microcontroller, a Danfoss MBS 3000 pressure sensor, and a flow meter deployed at three locations to collect data for three months. To model the relationship between pressure and flow rate, ML algorithms such as linear regression (LR), support vector regression (SVR), and convolutional neural network (CNN) were trained, analysed, and compared. By establishing a model to estimate the flow rate based on pressure, the need for a flow meter in the setup can be eliminated. The system’s low-cost, easy-to-implement, and non-invasive nature makes it suitable for widespread adoption in residential areas, offering a promising solution for optimising water distribution and reducing water wastage.