Abstract
Visibility forecast is a meteorological problems which has direct impact to daily lives. For instance, timely prediction of low visibility situations is very important for the safe operation in airports and highways. In this paper, we investigate the use of Long Short-Term Memory(LSTM) model to predict visibility. By adjusting the loss function and network structure, we optimize the original LSTM model to make it more suitable for practical applications, which is superior to previous models in short-term low visibility prediction. In addition, there is a”time delay problem” when the number of hours time ahead we try to forecast becomes larger, this problem is persistent given the limited amount of available training data. We report our attempt of applying re-sampling to deal with the time delay problem, and we find that this method can improve the accuracy of visibility prediction, especially for the low visibility case.
Original language | English |
---|---|
Title of host publication | Proceedings of the 11th International Conference on Agents and Artificial Intelligence, ICAART 2019 |
Editors | Ana Rocha, Luc Steels, Jaap van den Herik |
Publisher | SciTePress |
Pages | 466-473 |
Number of pages | 8 |
Volume | 2 |
ISBN (Electronic) | 978-989-758-350-6 |
DOIs | |
Publication status | Published - 2019 |
Event | ICAART 2019: 11th International Conference on Agents and Artificial Intelligence - Prague, Czech Republic Duration: 19 Feb 2019 → 21 Feb 2019 Conference number: 11 http://www.icaart.org/?y=2019 |
Conference
Conference | ICAART 2019 |
---|---|
Country/Territory | Czech Republic |
City | Prague |
Period | 19/02/19 → 21/02/19 |
Internet address |
Keywords
- Atmospheric Visibility
- Time Series Forecast