Open AccessLabelling Data for Correlation Pollution Dataset by Using Machine Learning
Author(s) -
Huda W Ahmed,
Jameelah H Alamire
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1530/1/012028
Subject(s) - decision tree , c4.5 algorithm , pollution , computer science , air pollution , machine learning , binary classification , internet of things , class (philosophy) , artificial intelligence , air quality index , data mining , support vector machine , meteorology , naive bayes classifier , computer security , geography , chemistry , organic chemistry , ecology , biology
The significant problem in the environment is the pollution of air which affects all the areas in our world. The solution of this problem is excluding by using Internet of Things (IoT) technology which designed system that monitors the parameters that affect the pollution in the environment. The handling and operation of large quantities of information from a wide range of sources is challenges in IoT technology. The purpose of this study is to decrease the dimensions in IoT systems so we suggest two models for IoT system and also aims to build rules for Air quality classification by defining pollution levels. In this study, we suggested machine learning techniques to predict an air pollution levels based on a dataset consisting of daily weather, also we simplified our classification models to solve this problem by using binary classification which is to classify the air pollution level into “High pollution” and “Simple pollution” class labels. The decision tree J48 Algorithm gives the best outcomes in the first model than the second models.