Air Pollution Hazard Assessment Using Decision Tree
Keywords:Air pollution, Air Quality index, Machine Learning Algorithms, Neural Network, Support Vector Machine
The quality of air of a given region can be utilised as a primary determinant of air pollutants, as well as how well that the city's industry and population are controlled. With the development of industrialisation, monitoring urban air quality has become a persistent issue. All around the world, air quality has remained a severe concern for the government and the public. Air pollution has a notable impact on both the human health and the environment, culminating in acid rain, global climate change, heart problems, and melanoma. Utilizing two Machine Learning Algorithms, this study tackles the problem of forecasting the Air Quality Index (AQI) with the goal of reducing pollution before it becomes a problem.: Random Forest, Decision Tree, and Logistic Regression The Central Pollution Control Board (CPCB), Ministry of Environment, Forest and Climate Change, Government of India, provided the air pollution database. The proposed Machine Learning (ML) approach for predicting the Delhi AQI is impressive. The findings show an increase in predictive performance and imply that the model might be applied to other smart grids.
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