Hydro Sense: Empowering Water Quality Monitoring Through IoT And ML

Abstract

Clean water is an essential resource in sustaining
life, and ensuring the quality of drinking water is crucial for
promoting the wellbeing and health of individuals. Water
quality monitoring systems are essential for evaluating and
guaranteeing the safety of water sources. The current water
quality surveillance system lacks real-time information, which
is a drawback. Manually checking water quality continuously
is impractical. To address this issue, we have developed a
cost-effective live-stream water quality monitoring system
specifically for consumable water. Key factors such as
turbidity, Ph and temperature need to be measured to detect
contaminants and prevent water-related illnesses. Our system
includes specially designed sensors connected to a
microcontroller with an integrated ADC circuit for signal
conversion, data processing, and analysis. The hardware
component is connected to the main system via a USB cable.
The system displays the values of each parameters in the Blynk
console and when values are manually given to the trained
model it will predict if the water is in consumable form or not.
We have trained the model using the Random Forest
classification Algorithm to predict if the water is consumable
or not.