Visibility Forecast for Airport Operations by LSTM Neural Network

Tuo Deng, Aijie Cheng, Wei Han, Hai-Xiang Lin

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.

Download


Paper Citation


in Harvard Style

Deng T., Cheng A., Han W. and Lin H. (2019). Visibility Forecast for Airport Operations by LSTM Neural Network.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 466-473. DOI: 10.5220/0007308204660473


in Bibtex Style

@conference{icaart19,
author={Tuo Deng and Aijie Cheng and Wei Han and Hai-Xiang Lin},
title={Visibility Forecast for Airport Operations by LSTM Neural Network},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={466-473},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007308204660473},
isbn={978-989-758-350-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Visibility Forecast for Airport Operations by LSTM Neural Network
SN - 978-989-758-350-6
AU - Deng T.
AU - Cheng A.
AU - Han W.
AU - Lin H.
PY - 2019
SP - 466
EP - 473
DO - 10.5220/0007308204660473