Temporal Transfer Learning for Ozone Prediction based on CNN-LSTM Model

Tuo Deng, Astrid Manders, Arjo Segers, Yanqin Bai, Hai Lin

2021

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.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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