Author ORCID Identifier

https://orcid.org/0000-0002-2248-1701

Document Type

Conference Paper

Disciplines

1.2 COMPUTER AND INFORMATION SCIENCE

Publication Details

https://www.semanticscholar.org/paper/NeSDeepNet%3A-A-Fusion-Framework-for-Multi-step-of-Dey-Dev/e7be772df744d133f8018578d03e4a56c4c92bdd

Dey, Prasanjit et al. “NeSDeepNet: A Fusion Framework for Multi-step Forecasting of Near-surface Air Pollutants.” 2023 Photonics & Electromagnetics Research Symposium (PIERS) (2023): 1826-1834.

doi:10.1109/PIERS59004.2023.10221327

Abstract

Air pollution is a global issue that poses significant threats to human health and the environment due to industrial development. Forecasting the concentrations of major pollutants such as NO2 and CO can provide early warnings of harmful substances, minimizing health risks and losses. Recent deep learning models have shown promise in air quality prediction, but they have limitations such as insufficient feature representation, high computational costs, and poor generalization. This paper proposes a near-surface deep network (NeSDeepNet) to overcome these limitations. The NeSDeepNet integrates multiple deep learning models and a shallow model to form a hybrid forecasting system. The proposed framework consists of three modules: a preliminary extraction module, a deep extraction module, and a fusion module. The feature extraction module uses a multi-layer network to extract features from air pollutant and meteorological data, and each of which predicts air pollutants for different forecasting horizons. The fusion module combines the outputs of the deep learning module and the shallow models to produce the final prediction results. The proposed framework is evaluated on a real-world dataset, and the experimental results demonstrate that NeSDeepNet achieves optimal RMSE value of 9.59 for NO2 and 274.0 for CO, MAE value of 2.64 for NO2 and 13.75 for CO, and R2 values 0.89 for NO2 and 0.93 for CO, respectively, outperforming cutting-edge deep learning models. Therefore, NeSDeepNet can be a valuable tool for air quality forecasting and miti-gating the adverse effects of air pollution on human health and the environment. The source code for our proposed NeSDeepNet and comparative models is available on GitHub repository: https://github.com/Prasanjit-Dey/NeSDeepNet.

DOI

https://doi.org/10.1109/PIERS59004.2023.10221327

Funder

This research was funded by the Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality (d-real) under Grant No. 18/CRT/6224. This research was conducted with the financial support of Science Foundation Ireland under Grant Agreement No. 13/RC/2106 P2 at the ADAPT SFI Research Centre at University College Dublin. The ADAPT Centre for Digital Content Technology is partially supported by the SFI Research Centres Programme (Grant 13/RC/2106 P2) and is co-funded under the European Regional Development Fund.

Creative Commons License

Creative Commons Attribution-Share Alike 4.0 International License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.

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