Visibility Forecast for Airport Operations by LSTM Neural Network Tuo Deng1, Aijie Cheng1, Wei Han2 and Hai-Xiang Lin3 1School of Mathematics, Shandong University, Jinan, Shandong, 250100, China 2Numerical Weather Prediction Center of China Meteorological Administration, Beijing, 100081, China 3Delft Institute of Applied Mathematics, Delft University of Technology, Mekelweg 4, 2628 CD Delft, Netherlands Keywords: Atmospheric Visibility, Time Series Forecast. 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. 1 INTRODUCTION tors, and then calculates the visibility based on some empirical relationship with those factors. Most pre- Atmospheric visibility is the maximum horizontal vious researches are following such approach, and distance that a person with normal vision can distin- the methods of empirical fitting the interrelation bet- guish the target with sky as the background, which ween elements are mainly based on polynomial fitting is an important indicator to reflect the degree of air and traditional machine learning model. The polyno- pollution (Fan et al., 2016). In the case of rain and mial relationship between visibility, relative humidity snow and severe smog, the visibility can be very low, and aerosol concentration has been studied in the ci- which will greatly affect the safety of aviation, na- ties such as Shijiazhuang (Wang et al., 2016), Tianjin vigation and highway traffic. Visibility is influenced (Song et al., 2013) and Hangzhou (Fan et al., 2016). by a variety of meteorological factors, such as tempe- The visibility for highway in foggy weather is fit- rature, wind, precipitation, pressure, etc. In particu- ted by temperature, wind speed and humidity through lar, visibility shows strongly correlation with relative SVM and BP neural network (Long et al., 2017) in humidity, PM2.5, PM10 and so on. Traditional pre- past studies. However, the prediction results of these diction methods relying on physical modeling are in- methods are not accurate, and can only predict the ge- effective due to the complexity and inability to fully neral trend of visibility changes. quantify the influence of many different factors. For The second approach is to treat the visibility over instance, Clark et al. have investigated the problem of a period of time as a time series (Dietterich, 2002), prediecting visibility by numerical methods with the and solve the problem of time series prediction with Operational Met Office Unified Model(Clark et al., methods of machine learning or deep learning. For 2008). The results are not very accurate, especially instance, regression tree (Dietz et al., 2017) and MLP in case of low visibility due to insufficient spatial re- (Zhu et al., 2017) are studied for airport visibility fo- solution of the numerical grid. Visibility can change recast. abruptly in a scale of 10m, whereas the current nume- These two kinds of methods have their own ad- rical NWP models have a spatial resolution of 10km. vantages and disadvantages. The first method has bet- Currently, there are two main approaches to pre- ter interpretability due to the application of the actual dict the visibility. The first approach is based on physical model, but it is inaccurate due to the com- the numerical forecast of other meteorological fac- plexity and the lack of full understanding of the phe- 466 Deng, T., Cheng, A., Han, W. and Lin, H. Visibility Forecast for Airport Operations by LSTM Neural Network. DOI: 10.5220/0007308204660473 In Proceedings of the 11th International Conference on Agents and Artificial Intelligence (ICAART 2019), pages 466-473 ISBN: 978-989-758-350-6 Copyright c 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved Visibility Forecast for Airport Operations by LSTM Neural Network nomenon. Besides, this method is highly dependent rate predictions for 24-hour or longer like some areas on the prediction accuracy of other meteorological mentioned above. We try to fix it by resampling. elements. The second method only uses the meteo- rological data as input and few physical information as prior knowledge, which makes the model much 2 DATA AND ANALYSIS simpler. However, it does not deliver an explanation about the relationship between meteorological factors and the actual physical laws. In this section, we describe the specific information of data, which contains the elements and distribution In the past two decades, machine learning has at- of data. The spatiotemporal correlation of data is also tracted much attention and established their position analyzed. Besides, we fix the missing values through as important competitors of classical statistical in the spatial correlation and normalize the data. In addi- field of prediction (Kurt and Oktay, 2010). A num- tion, for the particularity of time series, we need to ber of methods have been widely used, such as SVM, reconstruct the input data. KNN, Decision Trees, etc (Friedman et al., 2001). These methods use only historical data to learn the random dependencies between the past and the fu- 2.1 Data Description ture. Among these methods, Recurrent Neural Net- work(RNN) can capture the characteristic of data in The data that we used is provided by China Meteo- sequence problems. Particularly, it has been applied rological Administration(CMA). Specifically, we use in time-series forecast problem (Yadav et al., 2013). the meteorological data of Beijing station ’54511’ from April 2016 to December 2017, which contains However, RNN models have their own shortco- 15143 sets of data. Each set corresponds to hourly mings. Traditional RNN models can not capture long- measurements, including PM10, PM2.5, temperature, term dependencies in the sequence of input data. To precipitation, pressure, relative humidity, wind speed, solve this problem, Long short-term memory(LSTM) wind direction and visibility. We choose the first neural network was developed. Compared with tra- 10000 sets of data as training set while the remaining ditional RNN models, LSTM can avoid the problem data as test set to verify the model. Spatially, we se- of gradient vanishing and caputre the long-term de- lect ten sites with relatively complete data around Bei- pendencies in time-series forecast problems. It has jing, among which Beijing station 54511 is chosen as been used in many fields, such as air pollutant pre- experimental data to construct time series, and the re- diction (Li et al., 2017), earthquake prediction (Wang maining nine sites are used to interpolation missing et al., 2017), stock price prediction (Minami, 2018) data. Figure 1 shows the location of all ten meteoro- and internet traffic prediction (Cortez et al., 2006), logical observation stations that we use, and Beijing etc. LSTM has also been used for visibility prediction station is marked in red. in previous studies(Salman et al., 2018). However, the result is of limited practical significance since they focused only on overall errors(RMSE) and did not pay attention to the accuracy of low-visibility fore- cast, which is precisely the most relevant and difficult part in practical application. This paper aims to use LSTM to make visibility predictions which is a problem with properties dif- ferent from the aforementioned applications. Speci- fically, we consider visibility forecast of 1 hour re- spectively and 3 hours ahead. Compared with the commonly used visibility prediction models in previ- ous studies, the LSTM model has significantly impro- ved, which is more accurate in cases of low visibility. Figure 1: Location of stations in Beijing. Because low visibility is more concerned in practice, we design a weighted loss function to optimize the In order to understand the distribution of data bet- model. In order to make predictions of many hours ter, we segment the existing data into bar charts in more(e,g., 6 or 8 hours ahead), we find that there is Figure 2, where we use intervals of 1,000 meters. a systematic time delay in forecast result, which can We can see that in the existing two-year data, visi- be caused by insufficient data. This also leads to the bility is concentrated in the range of 2,000 meters to inability of visibility prediction models to make accu- 4,000 meters. The occurrence of the visibility higher 467 ICAART 2019 - 11th International Conference on Agents and Artificial Intelligence than 3,000 meters gradually decreases. For the low- clear downward trend, which agrees with the com- visibility data which we are most concerned with, the mon sense that the closer events have greater influ- amount of existing data is also small, which makes ence. From Figure 3 we can see that autocorrelation it difficult to obtain sufficient training for these most is above 0.8 for visibility values within 3 hours, and interesting situations. then drops rapidly until it reaches 0.4 for visibility be- tween 12 hours. After that, autocorrelation slowly de- creases, reaching about 0.3 at 24 hour. We find that the meteorological information after 24 hours is basi- cally not related to the current stage. Therefore, we decide to use the data of the past 24 hours as a single input item for the model.