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<p>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-optimised XGBoost learner, and (iv) fusing both outputs to obtain the final prediction. The framework is assessed on test and out-of-sample datasets and benchmarked against SVR, BiGRU, random forest, BiLSTM, and GRU. On the test set, SSA-LSTM-XGBoost attains the highest accuracy, recording an R<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(^{\varvec{2}}\)</EquationSource> </InlineEquation> of 0.9554 and the lowest errors (RMSE = 2.5194, MAE = 1.4885, MAPE = 0.0635), amounting to an average error reduction of roughly 8.2% relative to the runner-up SSA-SVR. When validated on unseen data, it remains superior (R<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(^{\varvec{2}}\)</EquationSource> </InlineEquation> = 0.8830, RMSE = 2.4838), achieving an average error decrease of about 3.5% compared with SSA-SVR despite the harsher evaluation conditions. These findings attest to the robustness and strong generalisability of the proposed framework for reliable PM<InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(_{\varvec{2.5}}\)</EquationSource> </InlineEquation> forecasting. In practice, such forecasts enable near-real-time hotspot alerts, short-term exposure advisories for vulnerable groups, and preventive traffic or industrial controls, thereby supporting municipal air-quality management and policy decisions in Duitama.</p>

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A hybrid LSTM-XGBoost model with residual correction for air quality prediction using SSA

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月22日

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  • Sergio Diaz,
  • Maria Fernanda Camargo,
  • JC Castiblanco,
  • Henry Mauricio Sánchez,
  • Johan S. Duque,
  • Ella Cecilia Escandón Dussan,
  • Omar F. Rojas-Moreno,
  • Alejandra Baena

摘要

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-optimised XGBoost learner, and (iv) fusing both outputs to obtain the final prediction. The framework is assessed on test and out-of-sample datasets and benchmarked against SVR, BiGRU, random forest, BiLSTM, and GRU. On the test set, SSA-LSTM-XGBoost attains the highest accuracy, recording an R \(^{\varvec{2}}\) of 0.9554 and the lowest errors (RMSE = 2.5194, MAE = 1.4885, MAPE = 0.0635), amounting to an average error reduction of roughly 8.2% relative to the runner-up SSA-SVR. When validated on unseen data, it remains superior (R \(^{\varvec{2}}\) = 0.8830, RMSE = 2.4838), achieving an average error decrease of about 3.5% compared with SSA-SVR despite the harsher evaluation conditions. These findings attest to the robustness and strong generalisability of the proposed framework for reliable PM \(_{\varvec{2.5}}\) forecasting. In practice, such forecasts enable near-real-time hotspot alerts, short-term exposure advisories for vulnerable groups, and preventive traffic or industrial controls, thereby supporting municipal air-quality management and policy decisions in Duitama.