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

Conference paper (2021)

Authors

T. Deng Mathematical Physics - EEMCS , Delft Institute of Applied Mathematics
Astrid Manders DIANA FEA
Arjo Segers DIANA FEA
Y. Bai Shanghai University, Mathematical Physics - EEMCS
H.X. Lin Delft Institute of Applied Mathematics , Mathematical Physics - EEMCS
Research Group
Mathematical Physics (EEMCS) (TU Delft)
Transfer learning Short-term ozone prediction
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Published Date

2021

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Delft Institute of Applied Mathematics

Research Group

Mathematical Physics

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.