Ozone is a toxic gas with massive distinct chemical components from oxygen. Breathing ozone in the air can cause severe effects on human health, especially people who have asthma. It can cause long-lasting damage to the lungs and heart attacks and might lead to death. Forecasting the ozone concentration levels and related pollutant attribute is critical for developing sophisticated environment safety policies. In this paper, we present three artificial neural network (ANN) models to forecast the daily ozone (O 3 ), coarse particulate matter (PM 10 ), and particulate matter (PM 2.5 ) concentrations in a highly polluted city in the Republic of China. The proposed models are (1) recurrent multilayer perceptron (RMLP), (2) recurrent fuzzy neural network (RFNN), and (3) hybridization of RFNN and grey wolf optimizer (GWO), which are referred to as RMLP-ANN, RFNN, and RFNN-GWO models, respectively. The performance of the proposed models is compared with other conventional models previously reported in the literature. The comparative results showed that the proposed models presented outstanding performance. The RFNN-GWO model revealed superior results in the modeling of O 3 , PM 10 , and PM 2.5 compared with the RMLP-ANN and RFNN models.

中文翻译：

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用于预测中华民国臭氧和颗粒物浓度的混合神经网络模型

臭氧是一种有毒气体，含有大量与氧气不同的化学成分。吸入空气中的臭氧会对人类健康造成严重影响，尤其是患有哮喘的人。它会对肺和心脏病造成长期损害，并可能导致死亡。预测臭氧浓度水平和相关污染物属性对于制定复杂的环境安全政策至关重要。在本文中，我们提出了三种人工神经网络 (ANN) 模型来预测中华民国某高污染城市的每日臭氧 (O 3 )、粗颗粒物 (PM 10 ) 和颗粒物 (PM 2.5 ) 浓度. 提出的模型是（1）循环多层感知器（RMLP），（2）循环模糊神经网络（RFNN），以及（3）RFNN和灰狼优化器（GWO）的混合，它们分别被称为 RMLP-ANN、RFNN 和 RFNN-GWO 模型。将所提出模型的性能与文献中先前报道的其他传统模型进行了比较。比较结果表明，所提出的模型表现出优异的性能。与 RMLP-ANN 和 RFNN 模型相比，RFNN-GWO 模型在 O 3 、PM 10 和 PM 2.5 的建模中显示出优异的结果。