Print Email Facebook Twitter Temporal transfer learning for ozone preddiction based on CNN-LSTM model Title Temporal transfer learning for ozone preddiction based on CNN-LSTM model Author Deng, T. Manders-Groot, A.M.M. Segers, A.J. Bai, Y. Lin, H.Q. Publication year 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. Subject Short-term Ozone PredictionTransfer LearningEnvironment & SustainabilityUrbanisation To reference this document use: http://resolver.tudelft.nl/uuid:c00c957d-514d-4b2c-bdf6-b71991fa502c TNO identifier 952683 Source Proceedings of the 13th International Conference on Agents and Artificial Intelligence - (Volume 2), February 4-6, 2021, 1-8 Document type conference paper Files To receive the publication files, please send an e-mail request to TNO Library.