article · Journal of Emerging Science and Engineering · 2024

Hyperparameter optimization for hourly PM2.5 pollutant prediction

Aziz Jihadian Barid, Hadiyanto Hadiyanto

Cited by 8Open access

Abstract

Air pollution, particularly the presence of Particulate Matter (PM) 2.5, poses significant health risks to humans, with industrial growth and urban vehicle emissions being major contributors. This study utilizes machine learning techniques, specifically K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms, to predict PM2.5 levels. A dataset from Kaggle consisting of PM2.5 and other pollutant parameters is preprocessed and split into training and testing sets. The models are trained, evaluated, and compared using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics. Additionally, hyperparameters are applied to optimize the models. Results show that SVM with hyperparameters performs better, indicating its potential for accurate air quality prediction. These findings can aid policymakers in implementing effective pollution control strategies.

Cite this paper

APA 7

Barid, A. J., & Hadiyanto, H. (2024). Hyperparameter optimization for hourly PM2.5 pollutant prediction. Journal of Emerging Science and Engineering, 2(1), e15. https://doi.org/10.61435/jese.2024.e15

MLA 9

Barid, Aziz Jihadian, and Hadiyanto Hadiyanto. “Hyperparameter optimization for hourly PM2.5 pollutant prediction.” Journal of Emerging Science and Engineering, vol. 2, no. 1, 2024, p. e15, https://doi.org/10.61435/jese.2024.e15.

Chicago

Barid, Aziz Jihadian, and Hadiyanto Hadiyanto. 2024. “Hyperparameter optimization for hourly PM2.5 pollutant prediction.” Journal of Emerging Science and Engineering 2 (1): e15. https://doi.org/10.61435/jese.2024.e15.

Harvard

Barid, A.J. and Hadiyanto, H. (2024) ‘Hyperparameter optimization for hourly PM2.5 pollutant prediction’, Journal of Emerging Science and Engineering, 2(1), p. e15. Available at: https://doi.org/10.61435/jese.2024.e15.

BibTeX

@article{barid2024hyperparameter,
  title = {{Hyperparameter optimization for hourly PM2.5 pollutant prediction}},
  author = {Barid, Aziz Jihadian and Hadiyanto, Hadiyanto},
  journal = {Journal of Emerging Science and Engineering},
  volume = {2},
  number = {1},
  pages = {e15},
  year = {2024},
  doi = {10.61435/jese.2024.e15}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.