Advancing Spatiotemporal Pollutant Dispersion Forecasting with an Integrated Deep Learning Framework for Crucial Information Capture

Yuchen Wang, Zhengshan Luo (), Yulei Kong and Jihao Luo
Additional contact information
Yuchen Wang: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
Zhengshan Luo: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
Yulei Kong: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
Jihao Luo: School of Computer Science, Beijing Institute of Technology, Beijing 100081, China

Sustainability, 2024, vol. 16, issue 11, 1-19

Abstract: This study addressed the limitations of traditional methods in predicting air pollution dispersion, which include restrictions in handling spatiotemporal dynamics, unbalanced feature importance, and data scarcity. To overcome these challenges, this research introduces a novel deep learning-based model, SAResNet-TCN, which integrates the strengths of a Residual Neural Network (ResNet) and a Temporal Convolutional Network (TCN). This fusion is designed to effectively capture the spatiotemporal characteristics and temporal correlations within pollutant dispersion data. The incorporation of a sparse attention (SA) mechanism further refines the model’s focus on critical information, thereby improving efficiency. Furthermore, this study employed a Time-Series Generative Adversarial Network (TimeGAN) to augment the dataset, thereby improving the generalisability of the model. In rigorous ablation and comparison experiments, the SAResNet-TCN model demonstrated significant advances in predicting pollutant dispersion patterns, including accurate predictions of concentration peaks and trends. These results were enhanced by a global sensitivity analysis (GSA) and an additive-by-addition approach, which identified the optimal combination of input variables for different scenarios by examining their impact on the model’s performance. This study also included visual representations of the maximum downwind hazardous distance (MDH-distance) for pollutants, validated against the Prairie Grass Project Release 31, with the Protective Action Criteria (PAC) and Immediately Dangerous to Life or Health (IDLH) levels serving as hazard thresholds. This comprehensive approach to contaminant dispersion prediction aims to provide an innovative and practical solution for environmental hazard prediction and management.

Keywords: air pollution; dispersion prediction; Residual Neural Network; Temporal Convolutional Network; sparse attention; Generative Adversarial Network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/11/4531/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/11/4531/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:11:p:4531-:d:1402658

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().