Predicting Momentary Happiness Towards Air Quality via Machine Learning

Subjective well-being (SWB) refers to people's subjective evaluation of their own quality of life. Previous studies show that environmental pollution, such as air pollution, has generated significant negative impacts on one's SWB. However, such works are often constrained by the lack of appropriate representation of SWB specifically related to air quality. In this study, we develop UMeAir, which collects one's real-time SWB, specifically, one's momentary happiness at a given air quality, pre-processes input data and detects outliers via Isolation Forests, trains and selects the best model via Support Vector Machine and Random Forests, and predicts the momentary happiness towards any air quality one experienced. Unlike traditional representation of air quality by pollution concentration/Air Pollution Index, UMeAir intends to represent air quality in a more user-comprehensible way, by connecting the air quality experienced at a particular time and location with the corresponding momentary happiness perceived towards the air. The higher the momentary happiness, the better the air quality one experienced. Our work is the first attempt to predict momentary happiness towards air quality in real-time, with the development of the-first-of-its-kind UMeAir Happiness Index (HAPI) towards air quality via machine learning.