Water quality plays an important role in human health. Contamination of
drinking water resources causes waterborne diseases like diarrhoea and even some
deadly diseases like cancer, kidney problems, etc. The mortality rate of waterborne
diseases is increasing every day and most school children get affected to a great extent.
Real-time monitoring of water quality of drinking water is a tedious process and most
of the existing systems are not automated and can work only with human intervention.
The proposed system makes use of the Internet of Things (IoT) for measuring water
quality parameters and recurrent neural networks for analysing the data. An IoT kit
using raspberry pi is developed and connected with a GPS module and proper sensors
for measuring pH, temperature, nitrate, turbidity, and dissolved oxygen. The measured
water quality data can be sent directly from raspberry pi to the database server or
through the mobile application by QR code scanning. Recurrent Neural Network
algorithms namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit
(GRU) are used for forecasting water quality. Results show that analysis made using
GRU is much faster than LSTM, whereas prediction of LSTM is slightly more accurate
than GRU. The data is categorized as poor, moderate, or good for drinking and it can
be accessed using smartphones through mobile application. In general, the proposed
system produces accurate results and can be implemented in schools and other drinking
water resources.
Keywords: Gated Recurrent Unit, Internet of Things, Long Short-Term Memory, Recurrent Neural Networks, Water Quality Parameters.
