Research On Air Quality Prediction Model And Its Application Based On Deep Learning

With the advancement of technology and the rapid development of urbanization and industrialization,air quality is constantly declining.Numerous pollutants generated in the production and daily life are scattered in the air and cannot be dealt with in a timely manner,seriously endangering people’s health.How to predict air quality more accurately in this situation has become a hot topic in current research.The accuracy of traditional prediction methods in longterm and short-term predictions are not high,and they are easily affected by meteorological conditions such as wind speed and rainfall,as well as seasonal changes.The actual prediction is often also limited by aspects such as incomplete datasets.In recent years,with the introduction of machine learning,deep learning,and neural networks,there have been significant improvements in air quality prediction.In response to the above issues,this article studies the current mainstream air quality prediction methods and various deep learning methods.Through analysis and experiments,a model suitable for air quality prediction in the current environment is proposed,as well as a method for predicting air quality based on seasonal changes in cities.The main research content is as follows:1.A model for seasonal prediction is designed to address seasonal issues,which includes a seasonal prediction module and a spatiotemporal attention mechanism.By working together with two modules,we can capture long-term or short-term trends in data,improve capture efficiency,and further identify the correlation between their feature information.By using a seasonal module network to further utilize the correlation of seasonal data features to increase prediction effectiveness,the impact of meteorological factors on seasonal prediction is explored.2.An improved spatiotemporal attention mechanism prediction model MA-LSTM is proposed to address the widespread lack of air quality data and current environmental factors and compared with the prediction effect of common models.The impact of meteorological factors on model prediction is analyzed.The number of layers and the number of neurons in each layer of the two-way model are adjusted to more fully extract the temporal correlation of air pollutants in the station and the correlation within the data characteristics.The addition of an improved forgetting layer reduces the overfitting problem of the model,and further improves the accuracy and robustness of the model by using the attention mechanism.Faced with the problem of missing data,study the impact of various commonly used filling methods on different models.In this paper,the air quality data set and meteorological characteristics data set of Beijing and Wuhan are used to design the corresponding experiments for the proposed model.In response to the impact of seasonal factors on prediction,experimental comparative analysis is conducted using seasonal modules to better utilize seasonal characteristics for accurate prediction of air quality,and the impact of meteorological factors on the model is analyzed.By comparing the prediction processes of common models and comparing the current air quality prediction results,the MALSTM model proposed in this article outperforms existing commonly used models in both longterm and short-term prediction effects and has good universality.It has good prediction performance for common data filling and prediction methods.