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Authors: Tuo Deng 1 ; Astrid Manders 2 ; Arjo Segers 2 ; Yanqin Bai 3 and Hai Lin 1

Affiliations: 1 Delft Institute of Applied Mathematics, Delft University of technology, Mekelweg 4, 2628 CD Delft, The Netherlands ; 2 TNO, Climate Air and Sustainability, Utrecht, 3584 CB, The Netherlands ; 3 Department of Mathematics, Shanghai University, Shanghai 200444, China

ISBN: 978-989-758-484-8

Keyword(s): Short-term Ozone Prediction, Transfer Learning.

Abstract: Tropospheric ozone is a secondary pollutant which can affect human health and plant growth. In this paper, we investigated transferred convolutional neural network long short-term memory (TL-CNN-LSTM) model to predict ozone concentration. Hourly CNN-LSTM model is used to extract features and predict ozone for next hour, which is superior to commonly used models in previous studies. In the daily ozone prediction model, prediction over a large time-scale requires more data, however, only limited data are available, which causes the CNN-LSTM model to fail to accurately predict. Network-based transfer learning methods based on hourly models can obtain information from smaller temporal resolution. It can reduce prediction errors and shorten run time for model training. However, for extreme cases where the amount of data is severely insufficient, transfer learning based on smaller time scale cannot improve model prediction accuracy.

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Paper citation in several formats:
Deng, T.; Manders, A.; Segers, A.; Bai, Y. and Lin, H. (2021). Temporal Transfer Learning for Ozone Prediction based on CNN-LSTM Model.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 1005-1012. DOI: 10.5220/0010301710051012

@conference{icaart21,
author={Tuo Deng. and Astrid Manders. and Arjo Segers. and Yanqin Bai. and Hai Lin.},
title={Temporal Transfer Learning for Ozone Prediction based on CNN-LSTM Model},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={1005-1012},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010301710051012},
isbn={978-989-758-484-8},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Temporal Transfer Learning for Ozone Prediction based on CNN-LSTM Model
SN - 978-989-758-484-8
AU - Deng, T.
AU - Manders, A.
AU - Segers, A.
AU - Bai, Y.
AU - Lin, H.
PY - 2021
SP - 1005
EP - 1012
DO - 10.5220/0010301710051012

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