Real time monitoring of water Quality using IoT and Deep learning

¹ Laboratoire Systèmes et Environnement Durables (SED), Faculté des sciences de l’ingénieur (FSI) Université Privée de Fès (UPF), Fez, Morocco
² Laboratoire Ingénierie Des Structures, Systèmes Intelligents Et Energie Electrique, ENSAM, Université Hassan II, Casalanca, Morocco

salouasenhaji@gmail.com

Abstract

Access to safe drinking water is one of the most pressing issues facing many developing countries. Water must meet Environmental Protection Agency (E.P.A.) requirements. The normal method of measuring physico-chemical parameters is to take samples manually and send them to the laboratory to check the water quality. In this paper, we proposed a new intelligent design of a real-time water quality monitoring system using Deep Learning technology. This system is composed of several sensors that allow us to measure water parameters (physico-chemical parameters), bacteriological parameters and organoleptic parameters) and to detect the presence of certain substances (undesirable substances, toxic substances) and of a single-board/mobile computer module, Internet and other accessories. Water parameters are automatically detected by the single-board computer. Raspberry Pi3 model B. The single board computer receives the data from the sensors and this data is sent to the web server using the Internet module. It is able to detect the water quality situation worldwide. The data will be analysed in real time. The application of deep learning to these areas has been an important research topic. The Long-Short Term Memory (LSTM) network has been shown to be well suited for processing and predicting large events with long intervals and delays in the time series. LSTM networks have the ability to retain long-term memory.