A classification and prediction based approach for real-time ETP outlet monitoring through E-IoT and remote sensing using machine learning and deep learning
Abstract
Water is a vital element in our environment but day by day water pollution is
increasing in an alarming rate in our country. In Bangladesh’s perspective, industries
such as textile and ready-made garments (RMG) contribute to a massive amount
of waste or effluent. Effluent treatment plant (ETP) are used to remove as much
suspended solids from wastewater as possible before it gets back to the environment.
However, according to a report published by the Environment and forests ministry,
seven state-run factories don’t have any effluent treatment plant (ETP) to treat their
waste before disposal. And also even the factories which has ETP do not always keep
the ETP up and running because it consumes a lot of electricity. The purpose of
our research is to establish a setup which will monitor the real-time quality of water
outside the industries and inform us whether the ETP is turned on or not with the
help of E-IoT and various classification algorithm. It will also predict the seasonal
impact where the ETP might be turned off again and what will be the quality of
water with the help of various machine learning and deep learning algorithms such
as CNN, KNN and LSTM. We have also tracking the sensor value for monitoring
and the ETP outlet with RGB color analysis. We have successfully achieved an
accuracy of 99% for KNN, 97.5% for CNN and 94.9% forecasting model accuracy
for LSTM.