Machine Learning Models for Drinking Water Quality Monitoring based on Sensor Data

点击翻译按钮获取中文摘要

Drinking water poisoning are not rare even in well developed countries like USA and Canada. Pollutants likeCaCO3, Sodium Chloride, Lead, K12 E.Coli, Fungal could enter our water system in every stage, water plant,pipe, or the end faucet. Hence it is not enough to monitor water quality only at the source. Historically it isimpossible or prohibitively expensive to install monitoring devices at the end user side. Only big entities likehospitals, food processing companies, etc. can afford water quality monitoring devices. Now the developmentof sensor technology makes it possible and economical to install sensors at each individual faucet. However,sensors at individual faucets are subject to great unknown factors and noises. After all, a kitchen is not aspotless scientific lab. This causes greater difficulties in classifying whether or not the drinking water undermonitoring is polluted or not. We need a new robust machine learning model and algorithm, which canaccurately trigger alarms such a noisy environment. The advancement of big data technologies and machinelearning algorithms make this otherwise impossible goal highly likely.