What does this research mean for the field?

An XGBoost model optimized with an Improved Grasshopper Optimization Algorithm (IGOA) achieves superior accuracy in predicting PM2.5 concentrations compared to standard regression and unoptimized boosting models. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This study aims to enhance PM2.5 concentration predictions using an advanced predictive model.

May 30, 2026Open Access

Optimized PM2.5 Prediction Model Using XGBoost Coupled with Improved Grasshopper Optimization Algorithm

Key Points

This study aims to enhance PM2.5 concentration predictions using an advanced predictive model.
Developed an optimized model combining XGBoost with an improved grasshopper optimization algorithm (IGOA).
Conducted comparative analysis of six regression models including XGBoost_GOA and proposed XGBoost_IGOA.
Analyzed model performance using various meteorological and chemical input variables.
Proposed model achieved MSE of 216 μg/m3, MAE of 5 μg/m3, MAPE of 15.34%, and R2 of 0.812.
Improvements in PM2.5 predictions indicate significant advancements over conventional methods.

Abstract

Accurate prediction of PM2. 5 concentrations is required to curb the risks of air pollution on human health. This study applies an approach to an extended gradient boosting model, advanced to an integrated improved grasshopper optimization algorithm, to predict PM2. 5, using both meteorological and chemical inputs. The advantages of XGBoost are utilized to manage obscure linkages and nonlinear interactions, whereas IGOA has the advantage of effectively optimizing hyperparameters (HPs). Therefore, we also conducted a comparative analysis between the following six regression models: linear regression (LR), random forest (RF), decision tree (DT), gradient boosting (GBoost), extended gradient boosting (XGBoost), and XGBoostGOA with the proposed XGBoostIGOA. The performance of the model was analyzed using various sets of input variables. According to the experimental data, the proposed model yields appreciable improvements in PM2. 5, with an MSE (μg/m3) of 216, MAE (μg/m3) of 5, MAPE (%) of 15. 34, and R2 of 0. 812. The results of this study suggest that the proposed method is a valuable tool for predicting PM2. 5 concentration accurately, and it may help other researchers in the execution of an improved prediction model in their respective areas.

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