A Multi-Step PM2.5 Time Series Forecasting Approach for Mining Areas Using Last Day Observed, Correlation-Based Retrieval, and Interpolation

Flores A.; Tito-Chura H.; Guzman-Valdivia J.; Morales-Gonzales R.; Flores-Quispe E.; Cuentas-Toledo O. · Computers (2025) · DOI: 10.3390/computers14110471

Resumen

Monitoring PM2.5 in mining areas is essential for air quality management; however, most studies focus on single-step forecasts, limiting timely decision making. This work addresses the need for accurate multi-step PM2.5 prediction to support proactive pollution control in mining regions. So, a new model for multi-step PM2.5 time series forecasting is proposed, which is based on historical data such as the last day observed (LDO), retrieved data by correlation levels, and linear interpolation. As case studies, data from three environmental monitoring stations in mining areas of Peru were considered: Tala station near the Cuajone mine, Uchumayo near the Cerro Verde mine, and Espinar near the Tintaya mine. The proposed model was compared with benchmark models, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). The results show that the proposed model achieves results similar to those obtained by the benchmark models. The main advantages of the proposed model over the benchmark models lie in the amount of data required for predictions and the training time, which represents less than 0.2% of that required by deep learning-based models.

Instituciones: Universidad Nacional de Moquegua