The Impact of Machine Learning: Harnessing the Power of Developing Water Quality Prediction System
DOI:
https://doi.org/10.18486/ijcsnt/14.2.007Keywords:
Machine Learning, Water Quality Prediction, LQPS, Random Forest Classifier, RFCAbstract
A water's distinctive colour is the result of the sun's rays interacting with the water's concentration and its constituents. If the water's colour changes, it means its qualities have changed, and the water is no longer fit for human consumption. Water contamination and natural catastrophes like floods and tsunamis have become more pressing issues in recent years. The use of polluted water is responsible for 35% of all fatalities on Earth. There is no safe way to drink polluted water, but one way to mitigate the problem is to check the water's predicted quality before drinking it. Utilizing filtration, water plants base their processes on characteristics such as pH, turbidity, temperature, hardness, etc., and with the help of IoT, which incorporates both software and hardware; it is possible to anticipate water quality. Predicting future water levels using machine learning is the major focus of this study. By analyzing the water's colour and quality, the system can determine if the sample is fit for consumption or other purposes. The model for water quality prediction is trained using Tensorflow, Keras, the suggested Learning based Quality Prediction System (LQPS), and Random Forest Classifier (RFC). This system is efficient, uses an image processing tool, and is cost-effective. It may be used as an initial level water quality check and works immediately. By utilizing mobile-captured and Google Earth photographs of water samples, it is possible to verify the proposed method of water quality prediction.
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