In low and middle-income countries, people die as a result of unhygienic water quality each year. The proposed method monitors stagnant water quality. Improving sanitation facilities by prior detection of contamination depends on both knowledge and resources (both microbiological and personnel). The proposed method uses Node MCU as core controller and various sensors to monitor the water quality. The micro controller will access the data from different sensors and then processes the data. Once the data is collected, the data is fed into machine learning models, and it is trained using machine learning algorithms (classification - SVM) or neural networks (ANN). Productive decision can be made out of the results from the model. Model will be trained using the parameters such as temperature, dissolved oxygen (D.O.), pH, biochemical oxygen demand (B.O.D), Nitrate-N and Nitrite-N, and fecal coliform. The outcome of the proposed work gives a complete report about contamination in the stagnant water and gives early alert to municipalities for preventing water-borne diseases.
TopIntroduction
In low and middle-income countries, over 8,20,000 people die as a result of inadequate water quality and hygiene each year, representing 61% of total diarrhoeal deaths. A major reason for the outbreak of these diseases is due to lack of sanitation in waste management, stagnant water and drinking water. Unfortunately there is no prevailing method that can effectively detect and monitor the outbreak and spreading of these diseases. In Current trend, there are a number of different possibilities that could suggest a waterborne outbreak, complaints about water quality and increase of AGI in the community, in general practices, or in hospitals (clinical surveillance) an increase of positive laboratory results indicating possible waterborne agents (laboratory surveillance).There is no technique that could detect the contamination in earlier stages and contamination /dumping of waste in water bodies is un-notified. Thus the early detection prevents the spreading of water-borne diseases thereby ensuring sanitation to create a healthy world. In this chapter, the possible solution is proposed for providing a healthy environment with a proper IOT based sanitation monitoring system using machine learning on stagnant water in various resources. The proposed monitoring system provides stable, real time, reliable and regional water quality monitoring in the stagnant water. The proposed system has IoT-enabled water sensors which can track the quality, portability, pressure, and temperature of wastewater.
Objective
The Sole objective is to monitor stagnant water quality and to improve sanitation facilities by prior detection of contamination depends on both knowledge and resources (both microbiological and personnel). This early detection prevents the spreading of water-borne diseases thereby ensuring sanitation to create a healthy world.
The researchers developed a system for real time monitoring of the water quality parameters. In their research they measure the water parameter such as turbidity, conductivity, temperature, ph and dissolved oxygen (N. Vijayakumar, 2013). Instead of arduino they used the raspberry pi b+ model as core controller and send the sensor data on cloud platform. Cloete, Malekain and Nair in their paper they designed and developed a water quality monitoring system which measures the physicochemical parameters of water such as flow, temperature, ph, conductivity, and the oxidation reduction potential. They detect the contamination of water by measuring the parameters and then send notification to the user. Sensors are connected to a microcontroller- based measuring node which processes and analyzes the data. In their paper, they used a zigbee transmitter and receiver module for communication between the measuring and notification nodes.
The system architecture with three layers for water quality monitoring system was proposed, in that the three are nodes for data monitorization, a base station and a remote station (Barabde, 2015). They connected all the layers with wireless communication protocol and data being send to the base station from data monitoring nodes via microcontroller. Collected data was displayed on a local host PC. Matlab was used to create a GUI (graphical User Interface) for data visualization and water parameters such as ph, turbidity, conductivity were displayed. If the compared value exceeds standard value a SMS will be sent to the client.
A self configurable, reusable and energy efficient WSN- based water quality monitoring system was considered about the monitoring of water quality (Nidal Nasser, 2013). Existing frameworks though have the applicability in water monitoring system, cannot be reused in other monitoring applications because of its static nature. Moreover this dynamic framework also improves the network life time, monitor the water quality real time, and store the information in a portal.
A Water Quality Monitoring System Using IOT consists of various physiochemical sensors which can measures the physical and chemical parameters of the water such as Temperature, Turbidity, pH and Flow. By these sensors, water contaminants are detected (Ankit Sharma, 2018). The sensor values processed by Raspberry pi and send to the cloud. The sensed data is visible on the cloud using cloud computing and the flow of the water in the pipeline is controlled through IoT.