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01-Applied Mathematics & Information Sciences
An International Journal


Volumes > Volume 18 > No. 1


Marine Air Quality Prediction Using Machine Learning

N Giridharan, S Makesh, M Jayasurya, A Dhanasekar.
With the use of three machine learning algorithms, SVM, DBSCAN, and Naive Bayes, the project "Marine Air Quality Prediction using SVM, DBSCAN, and Naive Bayes" intends to develop a model for predicting air quality in maritime environments. Based on a variety of environmental variables, such as city, date, PM 2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, O3, Benzene, Toluene, Xylene, and AQI, the model will categorize air quality. Data gathering, preprocessing, feature extraction, model training, and testing are a few of the processes that make up the project. The research demonstrates which one of these models results in high accuracy and F1 score for air quality prediction. The SVM-DBSCAN-Naive Bayes model, when used in combination, is very accurate in forecasting air quality levels in coastal areas and is beneficial for environmental monitoring and management. The project's contribution to environmental research is significant since it offers a workable method for forecasting maritime air quality.

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