Ensemble-SMOTE Model to Evaluate Air Quality in the Industrial Area in Chavara

Susymary Johnson; Deepalakshmi Perumalsamy

doi:10.63278/10.63278/mme.v31.1

Authors

Susymary Johnson Research Scholar, Department of Computer Applications, Kalasalingam Academy of Research and Education, India
Deepalakshmi Perumalsamy Professor, Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, India

DOI:

https://doi.org/10.63278/10.63278/mme.v31.1

Keywords:

Air Quality Evaluation, Machine Learning Techniques, Industry 4.0, and Chavara Kerala.

Abstract

Air quality is a critical environmental concern, particularly in industrial areas where emissions from factories can significantly impact the health of nearby populations. This study focuses on evaluating the air quality on pollutants like SO2, NO2, PM10, and SPM in the Kerala Minerals and Metals Limited (KMML) industrial area in Chavara, Kerala, India. To predict air quality indicators accurately, the researchers used a combination of artificial intelligence techniques. By comparing error metrics across different approaches, they identified the optimal method for accurate predictions. The study employed machine learning algorithms and SMOTE to predict Air Quality Index (AQI) levels. The ensemble SMOTE method outperformed individual classifiers like KNN, SVM, DT, RF, and GaussianNB, achieving higher accuracy, precision, recall, and F1-score, indicating its effectiveness in predicting AQI levels. The study also highlighted the importance of data preprocessing and balancing for improved prediction accuracy.

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