This paper aims to model the daily evolution for particulate matter concentrations of less than 1 μm (PM1), 2.5 μm (PM2.5), 4 μm (PM4), 10 μm (PM10), and PM-Total, based on weather factors (WF), by using the hybrid dragonfly-SVMr algorithm. Hourly data on atmospheric concentrations of PMi and WF were recorded simultaneously at an automatic air quality check station located at an urban site in Algiers, using the fine dust measurement device, Fidas® 200. The number of data collected on PM was 540 measurements. In this study, the meta-heuristic dragonfly algorithm (DA) was used in order to select the optimal hyper-parameters of the Support Vector Machine model. For this, a MATLAB® program based on the dragonfly optimization algorithm coupled with the SVM regression algorithm has been written in order to correlate for the PMi concentrations. The obtained results show that the established model has good predictive performance, with a coefficient of determination R2 = 0.98 and root of the mean square error RMSE = 1.9261.

中文翻译：

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使用混合蜻蜓-SVM算法预测PM1、PM2.5、PM4和PM10的浓度

本文旨在根据天气因素模拟小于 1 μm (PM1)、2.5 μm (PM2.5)、4 μm (PM4)、10 μm (PM10) 和 PM-Total 的颗粒物浓度的日常演变(WF)，通过使用混合蜻蜓-SVMr 算法。位于阿尔及尔市区的一个自动空气质量检查站使用细尘测量设备 Fidas® 200 同时记录了大气 PMi 和 WF 浓度的每小时数据。收集的 PM 数据数量为 540 次测量。在本研究中，元启发式蜻蜓算法 (DA) 用于选择支持向量机模型的最佳超参数。为此，编写了基于蜻蜓优化算法和 SVM 回归算法的 MATLAB® 程序，以便关联 PMi 浓度。