Journal of Software Engineering and Applications, 2019, 12, 423-431
https://www.scirp.org/journal/jsea
ISSN Online: 1945-3124
ISSN Print: 1945-3116
DOI:
10.4236/jsea.2019.1210026 Oct. 29, 2019 423 Journal of Software Engineering and Applications
Ground Ozone Level Prediction Using Machine
Learning
Zhiying Meng
Beijing National Day School, Beijing, China
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 environmen-
tal 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 fi-
nal 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 envi-
ronmentalists. 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 pollu-
tion, and its test scores can be compared with the previous five methods to
conclude the accuracy of Neuron Network.
Keywords
Ground Ozone Pollution, Machine Learning, Classification,
Logistic Regression, Decision Tree, Random Forest, AdaBoost,
Support Vector Machine
1. Introduction
Ground ozone pollution has been a serious air quality problem over the years
How to cite this paper:
Meng, Z.Y. (2019
)
Ground Ozone
Level Prediction Using Ma-
chine Learning
.
Journal of Software Eng
i-
neering
and Applications
,
12
, 423-431.
https://doi.org/10.4236/jsea.2019.1210026
Received:
September 11, 2019
Accepted:
October 26, 2019
Published:
October 29, 2019
Copyright © 201
9 by author(s) and
Scientific
Research Publishing Inc.
This work is licensed under the Creative
Commons Attribution
-NonCommercial
International License (
CC BY-NC 4.0).
http://creativecommons.org/licenses/by
-nc/4.0/
Open Access
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