Bio-Inspired Optimization of Fuzzy Inference Rules for Air Quality Prediction in an Ensemble Framework

Abstract

This study presents a novel ensemble learning approach designed to improve a fuzzy inference system (FIS) for forecasting PM2.5 pollution levels. The suggested model integrates the ensemble approach with a FIS to enhance predictive accuracy. By developing a collection of FIS, each trained on distinct subsets of the data, this method utilizes model diversity to enhance overall performance. Optimization algorithms are utilized to refine the FIS parameters, thereby improving the model’s predictive performance. The performance of the optimized ensemble FIS is assessed through the analysis of a real-world dataset concerning PM2.5 pollution levels. The findings demonstrate that the suggested approach surpasses the conventional ensemble algorithm, such as the commonly utilized Random Forest, in terms of accuracy and robustness. The optimized ensemble FIS presents a compelling approach for accurate air quality forecasting, highlighting its significance as an essential instrument for environmental assessment and safeguarding public health.