Research On Air Quality Feature,Influencing Factors,and Air Quality Prediction Algorithm

Since people started paying attention to air quality issues,air quality prediction has been a subject of intense research among many scholars.How to accurately,quickly,and effectively predict air quality and identify the factors that influence air quality for prediction has been a long-standing problem that has troubled scientists and researchers for years.In this study,we focus on these issues and conduct research using data analysis and mining techniques,as well as deep learning techniques.A large amount of historical air quality data was collected in the experiments,serving as the foundation for this study,which can reveal the relationship between air quality and various factors.Data analysis and mining techniques were applied to process and analyze the data in order to discover hidden features.Through this approach,factors with significant impacts on air quality were identified to provide a basis for constructing the prediction model.To further improve the prediction accuracy,deep learning techniques were introduced and hierarchical modeling was conducted.Deep learning models were designed and trained to establish connections between patterns and trends in historical data and future air quality levels.Such models can learn complex nonlinear relationships and possess strong predictive capabilities.The key focuses of this study are as follows:(1)Factors influencing air quality: Data analysis and mining techniques were used to preprocess air quality data,conduct exploratory data analysis,and create corresponding charts such as scatter plots,bar charts,line graphs,and heatmaps.Analysis of the feedback from these charts helps identify the true causes affecting the Air Quality Index(AQI).(2)Air quality prediction: Deep learning techniques were employed to model real air quality data over a certain period of time.This study proposes the LSTM-Time Air Q model based on a multi-layer LSTM and the In-Time Air Q model based on the Informer network for air quality prediction.Both models were trained and validated using a deep learning platform.(3)Improvement of air quality prediction models: The basic network structures of the two proposed air quality models were improved.For the LSTM-based air quality prediction model,this improvement mainly involves designing a network structure based on multi-layer LSTM blocks and incorporating a multi-layer attention mechanism module.This module effectively enhances the performance of the air quality prediction model,achieving good results on various evaluation metrics.For the improvement of the existing Informer network,this study introduces a spatial attention mechanism that integrates contextual features into its network structure.This attention mechanism,commonly used in object detection,is applied in this study to extract deep features from the data,aiming to enhance the short-term prediction capabilities of the Informer network.Finally,a conclusion is drawn based on the research and the feasibility of the two air quality prediction models in addressing air quality prediction problems is validated.