# Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model
## 1 Introduction
Especially when dealing with a very large amount of data and requiring accurate prediction, the prediction performance of statistical models becomes unsatisfactory. Amidst the escalation of air pollution and the swift advancement of deep learning technology, accurate air quality prediction has become increasingly imperative. Numerous predictive models grounded in profound learning principles have emerged in response. However, efficiently capturing spatiotemporal dependencies and improving the generalization ability of existing models remain significant challenges. For instance, [PERSON] and colleagues [20] introduced a predictive approach hinged on image quality analysis, although external factors and the quality of the captured images heavily influence its efficacy. For prediction purposes, [PERSON] and associates [21] employed the BPTT algorithm, tailored for Recurrent Neural Network (RNN) architectures. However, this model is susceptible to the well-documented issues of vanishing and exploding gradients, particularly over extended sequences, which impedes its capability to learn long-term dependencies. [PERSON] et al. [22] developed a CNN-LSTM hybrid deep neural network that leverages spatiotemporal correlations to estimate concentrations of air pollutants across multiple urban locales. Despite its sophisticated framework, the model's dependency on data, sensitivity to data quality, and voluminous hyperparameter optimization pose significant challenges. [PERSON] et al. [23] proposed a PSO-SVR model enhanced by particle swarm optimization, addressing the common PSO drawbacks of suboptimal global search efficiency and local minimum entrapment. [PERSON] and team [24] crafted a GA-ACO-BP neural network, converging the merits of genetic algorithms and ant colony optimization into BP neural networks. While this approach boosts model performance, it also decelerates model convergence. This study proposes a CNN-Bi-LSTM model based on adaptive inertia weight PSO optimization (APSO-CNN-Bi-LSTM) to address these issues and further improve prediction accuracy. The salient contributions of this study are twofold:  
1. The model can effectively combine CNN and Bi-LSTM to extract complex spatiotemporal dependencies in historical air quality data and focus on the key factors, thus providing higher accuracy in air quality prediction. 2. The introduction of adaptive inertia weights enabled the PSO algorithm to generalize different datasets better, implying that the model can be effectively applied to air quality prediction in other areas or periods. The structure of the subsequent chapters in this paper is outlined as follows: Section 2 introduces the geographical environment, climatic characteristics, and data sources pertinent to the study area. Section 3 details the APSO-CNN-Bi-LSTM model, including its design and implementation. Section 4 presents the experimental setup and results, comparing the proposed model's performance with that of other models and providing a thorough analysis of the findings. Finally, Section 5 offers concluding remarks, summarizing the study's contributions and implications. This structured layout ensures a comprehensive understanding of the research context, methodology, experimental evaluation, and conclusions drawn from the study.