A CNN-transformer framework for air quality forecasting to support aeolian dust management in river basins

doi:10.1016/j.aei.2025.103758

淡江大學機構典藏 > 工學院 > 人工智慧學系 > 期刊論文 > Item 987654321/128641

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/128641

Title:	A CNN-transformer framework for air quality forecasting to support aeolian dust management in river basins
Authors:	Kow, Pu-Yun;Hsu, Chia-Yu;Sun, Wei;Wang, Yun-Ting;Chang, Li-Chiu;Chang, Fi-John
Date:	2025-11
Issue Date:	2026-03-09 12:06:41 (UTC+8)
Abstract:	Accurately forecasting riverbed aeolian dust emissions (PM10) in complex watershed environments is a critical engineering challenge, shaped by the intricate interdependencies among hydrometeorological factors, land surface dynamics, and anthropogenic pollution sources. Traditional models often struggle to capture these nonlinear interactions, limiting their utility for real-time environmental decision-making. This study presents a novel hybrid deep learning framework—combining a 3D Convolutional Neural Network (CNN), dual 1D CNNs, and a Transformer architecture—to enhance the predictive accuracy and interpretability of PM1110 forecasts in Taiwan’s Jhuoshuei River Basin. The model harnesses the spatial feature extraction of the 3D CNN, temporal pattern recognition of the 1D CNNs, and long-range dependency modeling of the Transformer to learn complex, multiscale relationships across diverse environmental variables. Extensive quantitative and qualitative evaluations demonstrate the model’s superior performance over conventional approaches, particularly in capturing seasonal variability and the mitigating effects of water infrastructure (e.g., Jiji Weir discharge) on dust emissions. The model effectively anticipates pollution peaks, offering critical lead time for the implementation of targeted interventions such as reservoir releases or dust suppression. Beyond technical innovation, this research provides actionable insights into the dynamic coupling of atmospheric, hydrological, and operational factors. The model’s scalability and generalizability position it as a robust decision-support tool for engineers, environmental managers, and policymakers. By bridging AI-driven modeling with practical engineering applications, this study advances the field of environmental informatics and supports the development of adaptive, knowledge-based systems for sustainable air quality and watershed management.
Relation:	Advanced Engineering Informatics
DOI:	10.1016/j.aei.2025.103758
Appears in Collections:	[人工智慧學系] 期刊論文

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	77	View/Open

Loading...