Achieving accurate predictions of urban NO₂ concentration is essential for effectively control of air pollution. This paper selected the concentration of NO₂ in Tianjin as the research object, concentrating predicting model based on Discrete Wavelet Transform and Long- and Short-Term Memory network (DWT-LSTM) for predicting daily average NO₂ concentration. Five major atmospheric pollutants, key meteorological data, and historical data were selected as the input indexes, realizing the effective prediction of NO₂ concentration in the next day. Firstly, the input data were decomposed by Discrete Wavelet Transform to increase the data dimension. Furthermore, the LSTM network model was used to learn the features of the decomposed data. Ultimately, Support Vector Regression (SVR), Gated Regression Unit (GRU), and single LSTM model were selected as comparison models, and each performance was evaluated by the Mean Absolute Percentage Error (MAPE). The results show that the DWT-LSTM model constructed in this paper can improve the accuracy and generalization ability of data mining by decomposing the input data into multiple components. Compared with the other three methods, the model structure is more suitable for predicting NO₂ concentration in Tianjin.

中文翻译：

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基于离散小波变换和LSTM网络的区域NO2浓度预测的新方法

对城市NO ₂浓度进行准确的预测对于有效控制空气污染至关重要。本文以天津市NO ₂浓度为研究对象，基于离散小波变换和长短时记忆网络（DWT-LSTM）的浓度预报模型，用于预测日均NO ₂浓度。选择五种主要大气污染物，关键气象数据和历史数据作为输入指标，实现了对NO ₂的有效预测在第二天集中。首先，通过离散小波变换对输入数据进行分解，以增加数据的维数。此外，使用LSTM网络模型来学习分解数据的特征。最终，选择支持向量回归（SVR），门控回归单元（GRU）和单个LSTM模型作为比较模型，并通过平均绝对百分比误差（MAPE）评估每种性能。结果表明，本文构建的DWT-LSTM模型可以通过将输入数据分解为多个分量来提高数据挖掘的准确性和泛化能力。与其他三种方法相比，该模型结构更适合预测天津市的NO ₂浓度。