Papers Papers/2020



Authors: Tuo Deng 1 ; Aijie Cheng 1 ; Wei Han 2 and Hai-Xiang Lin 3

Affiliations: 1 School of Mathematics, Shandong University, Jinan, Shandong, 250100 and China ; 2 Numerical Weather Prediction Center of China Meteorological Administration, Beijing, 100081 and China ; 3 Delft Institute of Applied Mathematics, Delft University of Technology, Mekelweg 4, 2628 CD Delft and Netherlands

ISBN: 978-989-758-350-6

ISSN: 2184-433X

Keyword(s): Atmospheric Visibility, Time Series Forecast.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Big Data ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Engineering ; Data Management and Quality ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods

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.


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Paper citation in several formats:
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

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,},


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

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