Revisiting Random Forests in a Comparative Evaluation of Graph Convolutional Neural Network Variants for Traffic Prediction

<p>Cloud cover is a crucial information for many applications such as planning land observation missions from space. However, cloud cover remains a challenging variable to forecast, and Numerical Weather Prediction (NWP) models suffer from significant biases, hence justifying the use of statistical post-processing techniques. In our application, the ground truth is a gridded cloud cover product derived from satellite observations over Europe, and predictors are spatial fields of various variables produced by ARPEGE (M&#233;t&#233;o-France global NWP) at the corresponding lead time.</p><p>In this study, ARPEGE cloud cover is post-processed using a convolutional neural network (CNN). CNN is the most popular machine learning tool to deal with images. In our case, CNN allows to integrate spatial information contained in NWP outputs. We show that a simple U-Net architecture produces significant improvements over Europe. Compared to the raw ARPEGE forecasts, MAE drops from 25.1 % to 17.8 % and RMSE decreases from 37.0 % to 31.6 %. Considering specific needs for earth observation, special interest was put on forecasts with low cloud cover conditions (< 10 %). For this particular nebulosity class, we show that hit rate jumps from 40.6 to 70.7 (which is the order of magnitude of what can be achieved using classical machine learning algorithms such as random forests) while false alarm decreases from 38.2 to 29.9. This is an excellent result, since improving hit rates by means of random forests usually also results in a slight increase of false alarms.</p>

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A New Way of Airline Traffic Prediction Based on GCN-LSTM

Frontiers in Neurorobotics ◽

10.3389/fnbot.2021.661037 ◽

2021 ◽

Vol 15 ◽

Author(s):

Jiangni Yu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Air Traffic ◽

Traffic Prediction ◽

Long Term Memory ◽

Prediction System ◽

Short Term ◽

Prediction Ability ◽

Memory Network

With the development of society and the improvement of people's material level, more and more people like to travel by airplane. If we can predict the passenger flow of an airline in advance, it can be used as an important decision-making basis for its flight route planning, crew scheduling planning and ticket price formulation in the process of management and operation. However, due to the high complexity of aviation network, the existing traffic prediction methods generally have the problem of low prediction accuracy. In order to overcome this problem, this paper makes full use of graph convolutional neural network and long—short memory network to construct a prediction system with short—term prediction ability. Specifically, this paper uses the graph convolutional neural network as a feature extraction tool to extract the key features of air traffic data, and solves the problem of long term and short term dependence between data through the long term memory network, then we build a high-precision air traffic prediction system based on it. Finally, we design a comparison experiment to compare the algorithm with the traditional algorithms. The results show that the algorithm we proposed in this paper has a higher accuracy in air flow prediction according to the lower loss function value.

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