Machine learning-based Decision Tree J48 with grey wolf optimizer for environmental pollution control

Due to industrialization, activities of human and urbanization, environment is getting polluted. Air pollution has become a main issue in the metropolitan areas of the world. To protect people from diseases, monitoring air quality plays an important thing. This air pollutant may lead to many health...

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Bibliographic Details
Published in:Environmental technology 2023-06, Vol.44 (13), p.1973-1984
Main Authors: Hilal, Anwer Mustafa, Al-Wesabi, Fahd N., Alajmi, Masoud, Eltahir, Majdy M., Medani, Mohammad, Duhayyim, Mesfer Al, Hamza, Manar Ahmed, Zamani, Abu Sarwar
Format: Article
Language:English
Subjects:
Air monitoring
Air Pollutants - analysis
Air pollution
Air Pollution - prevention & control
Air quality
Air quality monitoring
Algorithms
Decision Tree j48
Decision Trees
Environmental Monitoring
environmental pollution control
grey wolf optimizer
Humans
Machine Learning
Maintenance costs
Metropolitan areas
Outdoor air quality
Pollutants
Pollution control
Pollution levels
Pollution monitoring
Urbanization
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Summary:Due to industrialization, activities of human and urbanization, environment is getting polluted. Air pollution has become a main issue in the metropolitan areas of the world. To protect people from diseases, monitoring air quality plays an important thing. This air pollutant may lead to many health issues like respiratory and cardiac problems. The major air pollutants are NO, C 6 H 6 , CO, etc. Many research works have been done in predicting air pollution-based health issues, predicting air pollution levels, monitoring and controlling the polluted levels. But they are not efficient, cost of maintenance is high and insufficient tool for monitoring it. To overcome these issues, this paper implements hybrid algorithm of Decision Tree J48 and Grey Wolf Optimizer (DT-GWO). This DT-GWO is a better model to addresses the predicting of Air Quality Index (AQI), which minimizes the error rate, accurately and effectively predicting the air quality. The AQI values are categorised as good, moderate, unhealthy, very unhealthy and hazardous. The dataset used in this work is collected from Kaggle website which contains air pollutants details with air quality index values. Accuracy obtained for decision Tree J48 is 93.72%, grey wolf optimizer is 96.83% and our proposed work DT-GWO is 99.78%.
ISSN:0959-3330
1479-487X
DOI:10.1080/09593330.2021.2017491