Air pollution monitoring is constantly increasing, giving more and more attention to its consequences on human health. Since Nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) are the major pollutants, various models have been developed on predicting their potential damages. Nevertheless, providing precise predictions is almost impossible. In this study, a new hybrid intelligent model based on long short-term memory (LSTM) and multi-verse optimization algorithm (MVO) has been developed to predict and analysis the air pollution obtained from Combined Cycle Power Plants. In the proposed model, long short-term memory model is a forecaster engine to predict the amount of produced NO₂ and SO₂ by the Combined Cycle Power Plant, where the MVO algorithm is used to optimize the LSTM parameters in order to achieve a lower forecasting error. In addition, in order to evaluate the proposed model performance, the model has been applied using real data from a Combined Cycle Power Plant in Kerman, Iran. The datasets include wind speed, air temperature, NO₂, and SO₂ for five months (May–September 2019) with a time step of 3-h. In addition, the model has been tested based on two different types of input parameters: type (1) includes wind speed, air temperature, and different lagged values of the output variables (NO₂ and SO₂); type (2) includes just lagged values of the output variables (NO₂ and SO₂). The obtained results show that the proposed model has higher accuracy than other combined forecasting benchmark models (ENN-PSO, ENN-MVO, and LSTM-PSO) considering different network input variables.

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中文翻译：

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基于深度学习模型和优化算法的空气污染预测应用

空气污染监测在不断增加，越来越关注其对人类健康的影响。由于二氧化氮（NO ₂）和二氧化硫（SO ₂）是主要污染物，因此已经建立了各种模型来预测其潜在危害。尽管如此，提供精确的预测几乎是不可能的。在这项研究中，基于长期短期记忆（LSTM）和多节优化算法（MVO）的新型混合智能模型已被开发出来，用于预测和分析联合循环电厂的空气污染。在提出的模型中，长短期记忆模型是预测器引擎，用于预测NO ₂和SO ₂的产生量由联合循环发电厂负责，其中MVO算法用于优化LSTM参数，以实现较低的预测误差。此外，为了评估建议的模型性能，已使用来自伊朗克尔曼联合循环发电厂的实际数据应用了该模型。数据集包括五个月（2019年5月至9月）的风速，气温，NO ₂和SO ₂，时间步长为3小时。另外，已经基于两种不同类型的输入参数对模型进行了测试：类型（1）包括风速，空气温度和输出变量（NO ₂和SO ₂）的不同滞后值；类型（2）仅包括输出变量的滞后值（NO ₂和SO₂）。获得的结果表明，与考虑不同网络输入变量的其他组合预测基准模型（ENN-PSO，ENN-MVO和LSTM-PSO）相比，所提出的模型具有更高的准确性。

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