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
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.