Prediction of air quality index based on the SSA-BiLSTM-LightGBM model

Abstract

The air quality index (AQI), as an indicator to describe the degree of air pollution and its impact on health, plays an important role in improving the quality of the atmospheric environment. Accurate prediction of the AQI can effectively serve people’s lives, reduce pollution control costs and improve the quality of the environment. In this paper, we constructed a combined prediction model based on real hourly AQI data in Beijing. First, we used singular spectrum analysis (SSA) to decompose the AQI data into different sequences, such as trend, oscillation component and noise. Then, bidirectional long short-term memory (BiLSTM) was introduced to predict the decomposed AQI data, and a light gradient boosting machine (LightGBM) was used to integrate the predicted results. The experimental results show that the prediction effect of SSA-BiLSTM-LightGBM for the AQI data set is good on the test set. The root mean squared error (RMSE) reaches 0.6897, the mean absolute error (MAE) reaches 0.4718, the symmetric mean absolute percentage error (SMAPE) reaches 1.2712%, and the adjusted R2 reaches 0.9995.

Enhancing COVID-19 Diagnosis Accuracy and Transparency with eXplainable Artificial Intelligence (XAI) Techniques

“…One such limitation involves the accuracy of symptom reporting, as individuals who tested positive for COVID-19 may be more likely to report their symptoms accurately [16], potentially leading to mislabeling among those who tested negative. This discrepancy is reflected in the reporting proportions of various symptoms, with certain symptoms more likely to be accurately reported by those who tested positive [18]. We identified symptoms such as headache, sore throat, and shortness of breath as potential sources of biased reporting, while symptoms like cough and fever demonstrated more balanced reporting [17].…”