Air pollution is one of the main environmental issues faced by most countries around the world. Forecasting air pollution occurrences is an essential topic in air quality research due to the increase in awareness of its association with public health effects, and its development is vital to managing air quality. However, most previous studies have focused on enhancing accuracy, while very few have addressed uncertainty analysis, which may lead to insufficient results. The fuzzy time-series model is a better option in air pollution forecasting. Nevertheless, it has a limitation caused by utilizing a random partitioning of the universe of discourse. This study proposes a novel Markov weighted fuzzy time-series model based on the optimum partition method. Fitting the optimum partition method has been done based on five different partition methods via two stages. The proposed model is first applied for forecasting air pollution using air pollution index (API) data collected from an air monitoring station located in Klang city, Malaysia. The performance of the proposed model is evaluated based on three statistical criteria, which are the mean absolute percentage error, mean squared error and Theil’s U statistic, using the daily API data. For further validation of the model, it is also implemented for benchmark enrolment data from the University of Alabama. According to the analysis results, the proposed model greatly improved the performance of air pollution index and enrolment prediction accuracy, for which it outperformed several state-of-the-art fuzzy time-series models and classic time-series models. Thus, the proposed model could be a better option for air quality forecasting for managing air pollution.

中文翻译：

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基于最优划分方法的马尔可夫加权模糊时间序列模型在空气污染预测中的应用

空气污染是世界上大多数国家面临的主要环境问题之一。由于人们越来越意识到空气污染与公共健康的关系，因此预测空气污染的发生是空气质量研究中必不可少的主题，其发展对管理空气质量至关重要。但是，以前的大多数研究都集中在提高准确性上，而很少涉及不确定性分析，这可能导致结果不足。在空气污染预测中，模糊时间序列模型是一个更好的选择。然而，它有一个局限，是由于利用话语空间的随机划分而引起的。本研究提出了一种基于最优划分方法的马尔可夫加权模糊时间序列模型。通过两个阶段，基于五种不同的分区方法完成了最佳分区方法的拟合。首先使用从马来西亚巴生市的空气监测站收集的空气污染指数（API）数据，将拟议模型应用于空气污染预测。基于三个统计标准对所提出模型的性能进行评估，这三个统计标准分别是平均绝对百分比误差，均方误差和Theil's使用每日API数据的U统计信息。为了进一步验证该模型，还针对阿拉巴马大学的基准招生数据实施了该模型。根据分析结果，提出的模型大大提高了空气污染指数的性能和入学预测准确性，在此方面，它的表现优于几种最新的模糊时间序列模型和经典时间序列模型。因此，提出的模型可能是用于管理空气污染的空气质量预测的更好选择。