Ground Ozone Level Prediction Using Machine Learning

Abstract

Because of the increasing attention on environmental issues, especially air pollution, predicting whether a day is polluted or not is necessary to people’s health. In order to solve this problem, this research is classifying ground ozone level based on big data and machine learning models, where polluted ozone day has class 1 and non-ozone day has class 0. The dataset used in this research was derived from the UCI Website, containing various environmental factors in Houston, Galveston and Brazoria area that could possibly affect the occurrence of ozone pollution [1]. This dataset is first filled up for further process, next standardized to ensure every feature has the same weight, and then split into training set and testing set. After this, five different machine learning models are used in the prediction of ground ozone level and their final accuracy scores are compared. In conclusion, among Logistic Regression, Decision Tree, Random Forest, AdaBoost, and Support Vector Machine (SVM), the last one has the highest test score of 0.949. This research utilizes relatively simple methods of forecasting and calculates the first accuracy scores in predicting ground ozone level; it can thus be a reference for environmentalists. Moreover, the direct comparison among five different models provides machine learning field an insight to determine the most accurate model. In the future, Neural Network can also be utilized to predict air pollution, and its test scores can be compared with the previous five methods to conclude the accuracy of Neuron Network.