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Home / Archives / Volume-5 / Issue-4 / Article-3

Volume - 5 | Issue - 4 | december 2023

PM2.5 Prediction using Heterogeneous Ensemble Learning
Shrabani Medhi  , Pallav Kashyap, Akansha Das, Jitjyoti Sarma
Pages: 481-498
Cite this article
Medhi, S., Kashyap, P., Das, A. & Sarma, J. (2023). PM2.5 Prediction using Heterogeneous Ensemble Learning. Journal of Artificial Intelligence and Capsule Networks, 5(4), 481-498. doi:10.36548/jaicn.2023.4.003
Published
13 November, 2023
Abstract

Air pollution is a great concern to mankind and is causing too many adverse effects on every living organism on earth by increasing lung diseases, skin diseases, and many other problems caused by it. This research presents a comprehensive study on the application of heterogenous ensemble learning techniques for PM2.5 concentration prediction, aiming to enhance prediction accuracy and provide insights into the driving factors behind pollution levels. The primary objective is to conduct a comparative analysis of heterogenous ensemble method, namely, blending and stacking in conjunction with individual base models, such as multiple linear regression (LR), decision trees (DT), support vector regression (SVR) and artificial neural networks (ANN). In total 28 models were created using blending and 28 models were created using stacking. Hyperparameter tuning is done to optimize the models.

Keywords

PM2.5 Prediction Support Vector Machine Decision Tree Multiple Linear Regression Artificial Neural Network Ensemble Learning Stacking Blending

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