IoT and ML Based Monitoring of Urban Wastewater System
|International Journal of Electronics and Communication Engineering|
|© 2020 by SSRG - IJECE Journal|
|Volume 7 Issue 6|
|Year of Publication : 2020|
|Authors : Jacob Paul P, Ashok T, Arun A, Dr. Nirmala Devi|
How to Cite?
Jacob Paul P, Ashok T, Arun A, Dr. Nirmala Devi, "IoT and ML Based Monitoring of Urban Wastewater System," SSRG International Journal of Electronics and Communication Engineering, vol. 7, no. 6, pp. 17-22, 2020. Crossref, https://doi.org/10.14445/23488549/IJECE-V7I6P103
The product mainly focuses on monitoring water quality (urban city wastewater) for agriculture purposes. Various water parameters such as pH, temperature, electrical conductivity, dissolved oxygen, turbidity, and color are considered for monitoring the water quality and the quantity of water. The location of the setup will also be recorded in the cloud. The nominal range in which various water parameters must be present to make the water suitable for agriculture was collected from the FAO website. The algorithm used is a Decision tree classifier (Machine Learning). A comparison between the observed value and the nominal range is made and can be viewed on the website and a mobile app that we developed. Both the website and the mobile app will display the harmful effect if the unsuitable water and the necessary steps to be taken to treat the water. The website and the mobile app are also available in different languages other than English, such as Hindi and Tamil. If toxic water suddenly comes, the motor will be switched off automatically, and an alert message will be sent to the end-user stating, “There was a sudden inflow of toxic water, and so the motor has been switched off.” Later the end-user can turn on the motor just by clicking a button in the mobile app.
Alarm alert system, Decision tree classifier (Machine Learning), Geotagged Sensors.
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