Visibility Prediction of Plateau Airport Based on LSTM

Zixuan, Wu; Qingchi, Ye; Zhihong, Yi; Wang, Yang; FENG, Zhiming

doi:10.1109/iaeac50856.2021.9391060

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Moreover, meteorological visibility is also temporal correlated, which can be predicted by some time-aware dynamic analysis techniques [28,29]. For example, Wu et al [30] utilize the environmental state information to achieve the visibility prediction results in the airport. In contrast, our method does not rely on temporal information and estimates the visibility from the real-time images.…”

Section: Image-based Visibility Estimationmentioning

confidence: 99%

Visibility estimation via deep label distribution learning in cloud environment

Song

Liu

et al. 2021

J Cloud Comp

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The visibility estimation of the environment has great research and application value in the fields of production. To estimate the visibility, we can utilize the camera to obtain some images as evidence. However, the camera only solves the image acquisition problem, and the analysis of image visibility requires strong computational power. To realize effective and efficient visibility estimation, we employ the cloud computing technique to realize high-through image analysis. Our method combines cloud computing and image-based visibility estimation into a powerful and efficient monitoring framework. To train an accurate model for visibility estimation, it is important to obtain the precise ground truth for every image. However, the ground-truth visibility is difficult to be labeled due to its high ambiguity. To solve this problem, we associate a label distribution to each image. The label distribution contains all the possible visibilities with their probabilities. To learn from such annotation, we employ a CNN-RNN model for visibility-aware feature extraction and a conditional probability neural network for distribution prediction. The estimation result can be improved by fusing the predicting results of multiple images from different views. Our experiment shows that labeling the image with visibility distribution can boost the learning performance, and our method can obtain the visibility from the image efficiently.

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Section: Image-based Visibility Estimationmentioning

confidence: 99%

Visibility estimation via deep label distribution learning in cloud environment

Song

Liu

et al. 2021

J Cloud Comp

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“…Sara et al [11] discussed an hourly short-term prediction of lowvisibility events at Spain Villanubla Airport with PM10, PM2.5, temperature, precipitation, pressure, relative humidity, wind speed and wind direction by Markov chain models and machine learning techniques. Wu et al [12] used the atmospheric state information of the airport ground station to build a plateau airports visibility prediction model to predict the hourly visibility of the plateau airports in the next 1-6 h by the LSTM. Liu et al [13] used data-driven depth learning method and multiple nonlinear regression analysis method to analyze the relationship between the runway visual range (RVR) and ground meteorological elements for different reasons and input the pseudo color images converted by the original images into the depth model integrated by two popular convolutional neural network (CNN) models: VGG-16 and Xception for analysis.…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Characteristics of Meteorological Elements in the Vertical Direction at Airports and Hourly Airport Visibility Prediction by Artificial Intelligence Methods

et al. 2022

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Based on second-level L-band sounding data, the vertical distribution and variation of meteorological elements at airports in 2010–2020 are investigated. At the same time, the relationships between airport visibility and meteorological elements at different potential heights are also investigated. Then, based on hourly measurements of 26 meteorological elements in 2018–2020, the hourly visibility of airports is predicted by 9 artificial intelligence algorithm models. The analyses show: (1) For the vertical changes in four meteorological elements of the airports, the negative vertical trends of temperature and relative humidity increase clearly from northwestern to southeastern China. The significant negative trend of air pressure in the vertical direction in the eastern China is greater. (2) Within about 2000 geopotential metres (gpm) from the ground, the visibility has a strong correlation with the air pressure, and most of them are negative. Within 400 gpm from the ground, airport visibility is negatively correlated with the relative humidity. At 8:00 a.m., airport visibility is positively correlated with the wind speed within 2000 gpm from the ground at most airports, while at 20:00 p.m., the positive correlation mainly appears within 400 gpm from the ground. (3) The passive aggressive regression-(PAR) and isotonic regression-(IST) based models have the worst effect on airport visibility prediction. The dispersion degree of the visibility simulation results obtained by Huber regression-(HBR) and random sample consensus regression-(RANSAC) based models is relatively consistent with the observations.

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Predicting Airport Visibility using Seq2Seq Models

Wang,

Wang

2023

2023 11th International Conference on Information Systems and Computing Technology (ISCTech)

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Visibility Prediction of Plateau Airport Based on LSTM

Cited by 4 publications

References 11 publications

Visibility estimation via deep label distribution learning in cloud environment

Visibility estimation via deep label distribution learning in cloud environment

Temporal and Spatial Characteristics of Meteorological Elements in the Vertical Direction at Airports and Hourly Airport Visibility Prediction by Artificial Intelligence Methods

Predicting Airport Visibility using Seq2Seq Models

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