Temporal transfer learning for ozone preddiction based on CNN-LSTM model

Temporal transfer learning for ozone preddiction based on CNN-LSTM model

Deng, T.
Manders-Groot, A.M.M.
Segers, A.J.
Bai, Y.
Lin, H.Q.

2021

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.

Short-term Ozone Prediction
Transfer Learning
Environment & Sustainability
Urbanisation

http://resolver.tudelft.nl/uuid:c00c957d-514d-4b2c-bdf6-b71991fa502c

952683

Proceedings of the 13th International Conference on Agents and Artificial Intelligence - (Volume 2), February 4-6, 2021, 1-8

conference paper