Long-Term PM2.5 Forecasting Using a DTW-Enhanced CNN-GRU Model

• URL: http://arxiv.org/abs/2510.22863v1
• Date: Sun, 26 Oct 2025 23:04:10 GMT
• Title: Long-Term PM2.5 Forecasting Using a DTW-Enhanced CNN-GRU Model
• Authors: Amirali Ataee Naeini, Arshia Ataee Naeini, Fatemeh Karami Mohammadi, Omid Ghaffarpasand,
• Abstract summary: Long-term forecasting of PM2.5 concentrations is critical for public health early-warning systems.<n>Existing deep learning approaches struggle to maintain prediction stability beyond 48 hours.<n>This paper presents a framework that combines DTW for intelligent station similarity selection with a CNN-GRU architecture.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable long-term forecasting of PM2.5 concentrations is critical for public health early-warning systems, yet existing deep learning approaches struggle to maintain prediction stability beyond 48 hours, especially in cities with sparse monitoring networks. This paper presents a deep learning framework that combines Dynamic Time Warping (DTW) for intelligent station similarity selection with a CNN-GRU architecture to enable extended-horizon PM2.5 forecasting in Isfahan, Iran, a city characterized by complex pollution dynamics and limited monitoring coverage. Unlike existing approaches that rely on computationally intensive transformer models or external simulation tools, our method integrates three key innovations: (i) DTW-based historical sampling to identify similar pollution patterns across peer stations, (ii) a lightweight CNN-GRU architecture augmented with meteorological features, and (iii) a scalable design optimized for sparse networks. Experimental validation using multi-year hourly data from eight monitoring stations demonstrates superior performance compared to state-of-the-art deep learning methods, achieving R2 = 0.91 for 24-hour forecasts. Notably, this is the first study to demonstrate stable 10-day PM2.5 forecasting (R2 = 0.73 at 240 hours) without performance degradation, addressing critical early-warning system requirements. The framework's computational efficiency and independence from external tools make it particularly suitable for deployment in resource-constrained urban environments.

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