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A hybrid LSTM-XGBoost model with residual correction for air quality prediction using SSANARA Subscribed
Air pollution is a major contributor to respiratory and cardiovascular diseases, prompting recent studies to adopt AI models for forecasting pollutant levels. In this work, we introduce a hybrid framework—SSA-LSTM-XGBoost—that applies a residual-correction strategy in four stages: (i) data preprocessing, (ii) training an LSTM network optimised with the Sparrow Search Algorithm (SSA), (iii) modelling the residuals with an SSA-o...
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