Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia.
Department of Mathematics and Statistics, Faculty of Science, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia.
Environ Monit Assess. 2024 May 8;196(6):523. doi: 10.1007/s10661-024-12700-4.
Air pollution events can be categorized as extreme or non-extreme on the basis of their magnitude of severity. High-risk extreme air pollution events will exert a disastrous effect on the environment. Therefore, public health and policy-making authorities must be able to determine the characteristics of these events. This study proposes a probabilistic machine learning technique for predicting the classification of extreme and non-extreme events on the basis of data features to address the above issue. The use of the naïve Bayes model in the prediction of air pollution classes is proposed to leverage its simplicity as well as high accuracy and efficiency. A case study was conducted on the air pollution index data of Klang, Malaysia, for the period of January 01, 1997, to August 31, 2020. The trained naïve Bayes model achieves high accuracy, sensitivity, and specificity on the training and test datasets. Therefore, the naïve Bayes model can be easily applied in air pollution analysis while providing a promising solution for the accurate and efficient prediction of extreme or non-extreme air pollution events. The findings of this study provide reliable information to public authorities for monitoring and managing sustainable air quality over time.
空气污染事件可以根据其严重程度分为极端事件和非极端事件。高危极端空气污染事件将对环境造成灾难性影响。因此，公共卫生和决策当局必须能够确定这些事件的特征。本研究提出了一种基于数据特征的概率机器学习技术，用于预测极端和非极端事件的分类，以解决上述问题。该研究提出在空气污染分类预测中使用朴素贝叶斯模型，利用其简单性以及高精度和高效率。对马来西亚巴生的空气污染指数数据进行了案例研究，时间范围为 1997 年 1 月 1 日至 2020 年 8 月 31 日。训练后的朴素贝叶斯模型在训练集和测试集上均取得了较高的准确率、灵敏度和特异性。因此，朴素贝叶斯模型可以很容易地应用于空气污染分析，同时为准确高效地预测极端或非极端空气污染事件提供了有前景的解决方案。本研究的结果为公共当局提供了可靠的信息，以便随着时间的推移监测和管理可持续的空气质量。
