IoT and ML-based Water Flow Estimation using Pressure Sensor
Abstract: 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.
Video URL: https://iiitaphyd-my.sharepoint.com/:v:/g/personal/maulesh_gandhi_research_iiit_ac_in/EbrhM9WZPwdMhKGP8ArM0u4BjdO4zQgDGU1JonA30kgH5A?e=Yv5x7Z
Repository URL: https://github.com/MauleshGandhi/Pressure_analysis
Submission Number: 3
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