Quick Clean Water: IoT and Machine Learning-Based Water Contamination Detection System

Quick Clean Water can detect water contamination in private wells, piped water, surface and used for agriculture, recreation, other purposes developed developing countries. Current testing systems are slow, costly, low availability, give back minimal results of 10-16 contaminants using expensive strips. is reusable, easy-to-use, portable, affordable, gives advanced presently 21 contaminants. First, the device calculates pH, turbidity, temperature, total dissolved solids, conductivity, salinity sensors. Inputting TDS, a machine learning model Random Ensemble algorithm predicts whether safe at 55% accuracy rate, which be improved through data augmentation. Several algorithms were tested evaluated by precision, recall, f-score, specificity, negative predictive value, rates. The hypothesis was that K-Means Clustering would result best model, but most efficient. If classified as non-potable, users enter odor, color, taste their into 99% accurate ML to identify exact contaminant researching more