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Predicting Wqi via Machine Learning

by Suyeong Hahn
Category: Environmental Science
Abstract – Rising temperatures from global warming cause water-related issues, especially in rural, distant areas lacking water quality monitoring infrastructure and personnel. To help assess drinking water quality efficiently, a study using machine learning was conducted, utilizing data from the Washington State Department of Ecology. Through EDA, three features associated with WQI were identified, and two experiments were run, comparing linear and extra tree regression. Linear regression was found to be better for small features, potentially reducing equipment and cost requirements for water quality prediction in undeveloped countries. The study also aims to incorporate the system into hardware for further development.
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