Water Disease Prediction Device
Akshay Mahendrakar, Shivam Kumar, Omkar Molak, Piyush Chitnis, Sonali Deshpande
Sensors, IoT, Drinking water chemistry indicators, Water Disease classification, Dataset, Machine learning.
This paper is basically based on the prediction of water diseases thus improving the liability of water and using the resource in a healthy manner. To do this we are using multiple sensors to measure the different parameters present in the water, the multiple sensors include pH sensor, turbidity sensor, and oxidation sensor. This will not only help us to improve the liability of the resource that we use daily but also prevent further chances of diseases. Here different sensors will work according to their respective functions. We are taking the analog input from the sensors and passing it to the system to examine the grade of water and detect the disease which we are going to find with the help of the dataset and prevent them at the initial stage. We are using Machine Learning techniques to develop and predict water quality. With the help of advanced technology, it is easy and fast to predict disease and inform the organization or user. As the amount of data keeps growing, an algorithm will become more accurate and predict faster. Different technologies like IoT and AI are used to build this project and predict the accurate output so it can be widely used.
Article Details
Unique Paper ID: 150537

Publication Volume & Issue: Volume 7, Issue 7

Page(s): 140 - 144
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