A hybrid approach leveraging meta-heuristic and ensemble learning for time-sensitive prediction of pollutant concentrations
摘要
Traditional deep learning models such as convolutional neural networks (CNNs), which capture localized features, and long short-term memory networks (LSTMs), which focus on long-term dependencies, often face challenges in achieving higher accuracy for time series prediction tasks. To address this limitation, this study proposes a hybrid deep learning model that integrates CNN, LSTM, the reptile search algorithm (RSA), and eXtreme Gradient Boosting (XGB) for pollutant concentration forecasting. Initially, the raw pollutant concentration data undergoes cleaning and normalization via a Min–Max scaler. The processed sequences are then separately fed into LSTM and CNN models to extract weighted features. RSA is applied to optimize these features, while XGB computes feature importance scores, quantifying the contribution of each selected feature to the predictive performance. The proposed model predicts pollutants such as