Particulate matter (PM2.5 and PM10) prediction using fourier series decomposition in combination with LSTM and SVM

Sustainable development globally is highly impacted by increased air pollution which is attributed to increasing population, commercial and industrial activities. Combustion gas emissions attributed to transportation, social and other activities are a major cause of air pollution. To mitigate adverse impact of air pollution on human health, forecasting PM₁₀ and PM2.5 is a necessity. This study employs Fourier series decomposition approach in combination with support vector machine and long short-term memory machine learning algorithms to predict PM₁₀ and PM2.5. Hourly data was obtained from December 2020 to November 2021 for Mohammedia city in Morocco. The model's performance was evaluated using RMSE, MAE and R². LSTMF and SVMF models in combination with Fourier series decomposition performed better than the SMV and LSTM standalone models. Hourly prediction of PM₁₀, LSTMF model performed better than other models during Autumn season with closely followed by the model in winter seasons. For PM2.5 prediction the model during autumn season was observed to be outperforming other models in all other seasons. These results were based on hourly prediction based on season. Then this study also, forecasted seven ahead for PM₁₀ and PM2.5. LSTMF model performed best with R² value of 0.95 (winter), 0.93 (spring), 0.85 (summer) and 0.96 (autumn) for PM2.5. For PM₁₀ the LSTMF performance was also good with R² value of 0.84 (winter), 0.92 (spring), 0.84 (summer), and 0.92 (autumn). This study highlights how hourly prediction can be achieved to identify in advance the patterns and trends for particulate matter concentration. This will aid the decision and policy makers to adopt mitigation measures and policy in advance to address air pollution issues during peak hours.