# The Power of Machine Learning Methods and PSO in Air Quality Prediction
## 1 Introduction
[12] used an optimized machine learning model combining grey wolf optimization (GWO) and decision tree (DT) algorithms for the accurate prediction of AQI in major cities of India. The proposed model achieved better prediction performance than traditional machine learning algorithms, with a maximum accuracy of 97.68% for Visakhapatnam City. [PERSON] and [PERSON] [13] used the ELM + PSO algorithm to predict AQI. In the study, ELM model estimation was performed, and the output weights of the limit learning machine were used for a PSO optimization search. According to the results, the proposed model achieved high accuracy in short-term air quality prediction. In another study, [PERSON] et al. [14] developed a genetic algorithm-based improved extreme learning machine (GA-KELM) forecasting method. Comparative experiments with baseline data collected from a city in China were conducted based on three machine learning methods. The results showed that the proposed model was trained faster and made more accurate predictions. In the literature, the air quality prediction performance of widely used machine learning models has been examined in detail. Still, there are limited studies on improving these models with optimization techniques. This study examined how well PSO (particle swarm optimization) and conventional machine learning techniques predicted air quality. Widely used traditional machine learning models such as XGBoost, support vector regressor (SVR), linear regression (LR), and random forest (RF) were preferred. The models were trained on an open access dataset,and performance evaluation was performed. The support vector regression model with PSO and hyperparameter optimization obtained the best result, with an RMSE of 0.074. The effect of each feature on the best model's performance was also displayed once SHAP was applied. The contributions of this study are listed as follows. * The performance of the SVR model optimized with PSO is evaluated. * Widely used models such as XGBoost, random forest (RF), linear regression (LR), and support vector regression (SVR) are compared. * The effect of PSO on hyperparameter tuning is analyzed to show how the prediction accuracy can be improved compared with studies with default parameters. In particular, the RMSE value of 0.122 obtained by the SVR model after optimization clearly demonstrates performance improvement. * The effect of the variables used in air quality forecasting on the forecast accuracy is analyzed by the SHAP method. This way, which variables are more decisive in air quality prediction is shown. * The PSO-optimized SVR model has been shown to have the lowest error rate, which results in more accurate air quality forecasts. These results provide an essential contribution to the development of a reliable model that can be used in environmental monitoring systems. * The dataset used in the study is open access and, thus, it can be replicated and extended by different researchers. The remainder of this study is organized as follows. Section 2, the dataset used, machine learning algorithms, PSO, and SHAP methods are presented in Section 2. The suggested approach is presented in Section 3. This study's experimental findings are shown in Section 4. Section 5, where this study's findings are evaluated. This study's conclusions are presented in Section 6.