Hyperparameter optimization for hourly PM2.5 pollutant prediction
DOI:
https://doi.org/10.61435/jese.2024.e15Abstract
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.
Keywords:
PM2.5, K-Nearest Neighbor, Support Vector Machine, HyperparameterDownloads
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Akhter, S., & Miller, J. H. (2023). BaPreS: a software tool for predicting bacteriocins using an optimal set of features. BMC Bioinformatics, 24(1). https://doi.org/10.1186/s12859-023-05330-z
Arora, P., Periwal, N., Goyal, Y., Sood, V., & Kaur, B. (2023). iIL13Pred: improved prediction of IL-13 inducing peptides using popular machine learning classifiers. BMC Bioinformatics, 24(1). https://doi.org/10.1186/s12859-023-05248-6
Biancofiore, F., Busilacchio, M., Verdecchia, M., Tomassetti, B., Aruffo, E., Bianco, S., Di Tommaso, S., Colangeli, C., Rosatelli, G., & Di Carlo, P. (2017). Recursive neural network model for analysis and forecast of PM10 and PM2.5. Atmospheric Pollution Research, 8(4), 652–659. https://doi.org/10.1016/j.apr.2016.12.014
Budi Santosa, & Ardian Umam. (2018). Data Mining Dan Big Data Analytics : Teori dan implementasi mengunakan Python & Apache Spark (2nd ed.). Penebar Media Pustaka.
Kennial Laia. (2022, March). Laporan IQAir: Indonesia Peringkat ke-17 Negara Paling Berpolusi. Https://Betahita.Id/News/Detail/7310/Laporan-Iqair-Indonesia-Peringkat-Ke-17-Negara-Paling-Berpolusi.Html.Html.
Pak, U., Ma, J., Ryu, U., Ryom, K., Juhyok, U., Pak, K., & Pak, C. (2020). Deep learning-based PM2.5 prediction considering the spatiotemporal correlations: A case study of Beijing, China. Science of the Total Environment, 699. https://doi.org/10.1016/j.scitotenv.2019.07.367
Pedregosa FABIANPEDREGOSA, F., Michel, V., Grisel OLIVIERGRISEL, O., Blondel, M., Prettenhofer, P., Weiss, R., Vanderplas, J., Cournapeau, D., Pedregosa, F., Varoquaux, G., Gramfort, A., Thirion, B., Grisel, O., Dubourg, V., Passos, A., Brucher, M., Perrot andÉdouardand, M., Duchesnay, andÉdouard, & Duchesnay EDOUARDDUCHESNAY, Fré. (2011). Scikit-learn: Machine Learning in Python Gaël Varoquaux Bertrand Thirion Vincent Dubourg Alexandre Passos PEDREGOSA, VAROQUAUX, GRAMFORT ET AL. Matthieu Perrot. In Journal of Machine Learning Research (Vol. 12). http://scikit-learn.sourceforge.net.
Sinolungan, J. S. V, Psikologi, B., Kedokteran, F., Sam, U., & Manado, R. (n.d.). DAMPAK POLUSI PARTIKEL DEBU DAN GAS KENDARAAN BERMOTOR PADA VOLUME DAN KAPASITAS PARU.
Umri, S. S. A., Firdaus, M. S., & Primajaya, A. (2021). ANALISIS DAN KOMPARASI ALGORITMA KLASIFIKASI DALAM INDEKS PENCEMARAN UDARA DI DKI JAKARTA. Jurnal Informatika Dan Komputer) Akreditasi KEMENRISTEKDIKTI, 4(2). https://doi.org/10.33387/jiko
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Journal of Emerging Science and Engineering published by BIORE Scientia Academy