Real time monitoring of water Quality using IoT and Deep learning

Saloua Senhaji,Mohamed Hamlich,Mohammed Ouazzani Jamil

doi:10.1051/e3sconf/202129701059

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.

Highlights

The surface water resources used for drinking water production in Morocco are different in nature depending on their origin and the anthropic impacts they receive
This manuscript presents a new design of a real-time water quality monitoring system
The system is composed of several sensors that will be used to measure chemical parameters of water such as organoleptic parameters, physico-chemical parameters, undesirable substances, toxic substances

Summary

Introduction

The surface water resources used for drinking water production in Morocco are different in nature depending on their origin and the anthropic impacts they receive. Drinking water producers aim to provide consumers with water that will not pose a risk to human health and that will be compatible with Moroccan food water quality standards [1, 2] These standards impose quality requirements on surface water used for drinking water production for a given level of treatment. New environmental policies require improved methods for the study and assessment of well water quality, in this context we propose an intelligent well monitoring technique to automate and computerise water policing tasks In this context, this manuscript presents a new design of a real-time water quality monitoring system. The system will generate large amounts of data that we will be forced to process in real time, to solve this problem we propose deep learning techniques. In an LTSM network, a stacked LSTM hidden layer allows learning a high-level temporal feature without the need for a hidden layer and without the need for fine-tuning and pre-processing that would be required by other techniques

PROBLEM DEFINITION

PROPOSED METHODOLOGY

Water quality

Deep Learning for Anomaly Detection in IoT Data

The LSTM-Gauss-NBayes model to detect anomalies

Conclusion