A hybrid Machine Learning approach for air quality prediction in Morocco: combining CatBoost with metaheuristic optimization algorithms

Air pollution poses serious risks to public health and environmental sustainability, particularly in rapidlyurbanizing areas of developing countries. This study investigates whether combining machine learning algorithms withmetaheuristic optimization techniques can improve the accuracy and efficiency of air quality prediction in Morocco. Themain objective is to compare direct classification of Air Quality Index (AQI) categories with a regression-based approach,and to evaluate the effectiveness of two optimization strategies—Arithmetic Optimization Algorithm (AOA) and HungerGames Search (HGS)—in tuning the CatBoost model’s hyperparameters. Using five months of air quality data from twomonitoring stations in Ait Melloul, we modeled concentrations of PM2.5, PM10, CO, and derived corresponding AQIclassifications. The hybrid approach demonstrated that regression-based classification improved accuracy by nearly 30percentage points over direct classification. Moreover, HGS achieved similar predictive performance to AOA but was overtwice as computationally efficient. CO concentration predictions in residential areas achieved high accuracy (R2 > 0.95),while particulate matter predictions revealed limitations in capturing extreme pollution events. These findings suggest thatcombining gradient boosting with metaheuristic optimization is a promising strategy for developing scalable and accurate airquality forecasting systems in North African urban environments, with important implications for public health protectionand environmental policy implementation