Predictive Modeling of Air Quality Index Using Ensemble Learning and Multivariate Analysis

Anggina Primanita, Hadipurnawan Satria

Abstract

Breathing polluted air can result in multiple health problems. Thus, it is important to understand and predict the air quality in the environment. Air Quality Index (AQI) is a unit used to measure the air pollutants. In Indonesia, this value is measured and published by the Meteorological, Climatological, and Geophysical Agency regularly. In this research, four commonly used regression algorithms were used to analyzed AQI data, namely, Random Forest, Decision Tree, K-Neural Network, and Ada Boost. All the algorithms model were developed to analyzed 1096 AQI data. The Mean Squared Error value of each model was computed as a measure of comparison. It is found that the Random Forest is the best performing algorithm. It can generalize well without overfitting to the data set.

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DOI: https://doi.org/10.36706/sjia.v5i2.121

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