Air quality indicators and AQI prediction coupling long-short term memory (LSTM) and sparrow search algorithm (SSA): A case study of Shanghai

Abstract

Air quality indicators and air quality index (AQI) prediction are effective approaches for urban decision-makers, planners, managers and even city residents to arrange their risk abatement measures in advance. In this study, five models, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Multi-layer perceptron (MLP), Long-short term memory (LSTM) and Long-short term memory coupling with sparrow search algorithm (LSTM-SSA), were used to predict hourly, daily and weekly concentrations of six air quality indicators (PM2.5, PM10, SO2, NO2, CO, O3) and AQI. The case study was based on the hourly observed data of Shanghai from February 1, 2021 to January 31, 2022 and prediction accuracy for different prediction models, indicators and periods was compared. Results revealed that: (1) The prediction accuracy of three neural network models (MLP, LSTM, LSTM-SSA) was superior to two tree-based models (RF, XGBoost) in terms of MAPE, RMSE, MAE and R2. (2) Comparing three neural network models, LSTM-SSA was more accurate than MLP and LSTM in terms of MAPE, RMSE, MAE and R2. (3) For LSTM-SSA, the MAPE of AQI was minimal (1.53%), followed by PM2.5 (2.72%), O3 (3.93%), PM10 (4.87%), NO2 (5.74%), CO (7.54%) and SO2 (8.82%). (4) For LSTM-SSA, the value of MAPE increased from 3.88%, 5.28%–5.91% when the prediction period increased from an hour, a day to a week.