The primary objective of this study is to predict network-wide multi-step-ahead flight delay. A novel graph-to-sequence learning architecture with attention mechanism (AG2S-Net) is developed to predict the multi-step-ahead hourly departure and arrival delay of the entire network. The proposed AG2S-Net consists of a graph convolution neural network, a bi-LSTM neural network, and a sequence-to-sequence framework with embedded attention mechanism. Five-year flight data of 75 airports and 242 links are collected from the National Airspace System to illustrate the procedure. First, K-means clustering algorithm is applied to classify the hourly flight delay states of the entire network into four typical delay patterns, which are further used as explanatory variables in prediction model. Then, the network-wide multi-step-ahead flight delay prediction model is built with the proposed AG2S-Net. The result indicates that the model achieves better performance on large-scale airports than small-scale airports. The delay pattern variables and weather variables can greatly improve the prediction performance. In addition, compared with several benchmark methods, the AG2S-Net performs the best for all three airport types in terms of the lowest RMSE and MAE values. Finally, the graph network analysis further reveals that the proposed AG2S-Net can capture the hidden correlations between airports without links and collect information from more airports in the same community to enhance the prediction performance. The results of this study could provide insightful suggestions for aviation authorities and airport regulators to develop effective air traffic control strategies for alleviating flight delays and improving operation efficiency of the entire network.

Graph to sequence learning with attention mechanism for network-wide multi-step-ahead flight delay prediction

Traditional statistical, physical, and correlation models for chaotic time series prediction have problems, such as low forecasting accuracy, computational time, and difficulty determining the neural network’s topologies. Over a decade, various researchers have been working with these issues; however, it remains a challenge. Therefore, this review paper presents a comprehensive review of significant research conducted on various approaches for chaotic time series forecasting, using machine learning techniques such as convolutional neural network (CNN), wavelet neural network (WNN), fuzzy neural network (FNN), and long short-term memory (LSTM) in the nonlinear systems aforementioned above. The paper also aims to provide issues of individual forecasting approaches for better understanding and up-to-date knowledge for chaotic time series forecasting. The comprehensive review table summarizes the works closely associated with the mentioned issues. It includes published year, research country, forecasting approach, application, forecasting parameters, performance measures, and collected data area in this sector. Future improvements and current studies in this field are broadly examined. In addition, possible future scopes and limitations are closely discussed.

/pdf/chaotic-time-series-forecasting-approaches-using-machine-2d33ozrk.pdf

Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review

Abstract The importance of robust flight delay prediction has recently increased in the air transportation industry. This industry seeks alternative methods and technologies for more robust flight delay prediction because of its significance for all stakeholders. The most affected are airlines that suffer from monetary and passenger loyalty losses. Several studies have attempted to analysed and solve flight delay prediction problems using machine learning methods. This research proposes a novel alternative method, namely social ski driver conditional autoregressive-based (SSDCA-based) deep learning. Our proposed method combines the Social Ski Driver algorithm with Conditional Autoregressive Value at Risk by Regression Quantiles. We consider the most relevant instances from the training dataset, which are the delayed flights. We applied data transformation to stabilise the data variance using Yeo-Johnson. We then perform the training and testing of our data using deep recurrent neural network (DRNN) and SSDCA-based algorithms. The SSDCA-based optimisation algorithm helped us choose the right network architecture with better accuracy and less error than the existing literature. The results of our proposed SSDCA-based method and existing benchmark methods were compared. The efficiency and computational time of our proposed method are compared against the existing benchmark methods. The SSDCA-based DRNN provides a more accurate flight delay prediction with 0.9361 and 0.9252 accuracy rates on both dataset-1 and dataset-2, respectively. To show the reliability of our method, we compared it with other meta-heuristic approaches. The result is that the SSDCA-based DRNN outperformed all existing benchmark methods tested in our experiment. 

/pdf/social-ski-driver-conditional-autoregressive-based-deep-1poh7cf1.pdf

Social ski driver conditional autoregressive-based deep learning classifier for flight delay prediction

Deep learning (DL) has emerged as a powerful image processing technique that learns the features of the data and produces state-of-the-art prediction results. The decade from 2010 to 2020 is a real revival of DL, which has come to a turning point in history. In image classification, many deep learning networks have been proposed by scholars, and each of them has its own strengthness. It is very important and efficient for the researchers and the developers to know the performance of these networks, especially for the beginners, so as to give them a transplant instruction by an objective evaluation index. In this paper, we constructed different data sets from three aspects, texture, shape, and measurement scale to test the performance of nine mainstream image classification networks. Cross-contrast experiments were performed to analyze the sensitivity of factors which influence the stability of image classification networks. Experimental results shown that in the 27 image datasets generated by the three image factors, the classification performance of AlexNet, GoogleNet, VggNet, and DenseNet is better. The perfomance comparison of these networks are showed and discussed in details. Code and pretrained models are available at https://github.com/liqiang12689/image-classification-finall .

/pdf/performance-evaluation-of-deep-learning-classification-3nyuwfov1t.pdf

Performance Evaluation of Deep Learning Classification Network for Image Features

Recently, the significance of accurate aircraft delay forecasting has grown in the aviation sector, which caused multi-billion-dollar losses faced by airlines and airports and passenger loyalty losses. Due to the importance of accurate flight delay prediction for all stakeholders involved, the aviation sector seeks to develop techniques for more robust flight delay prediction. Is quickly becoming an important research issue to improve airport and airline service performance and offer customers dependable travel itineraries. Machine learning techniques have been used in a number of studies to evaluate and resolve issues with flight delay prediction. This paper proposes a framework integrated network called 'Attention-based Bidirectional long short-term memory' (ATT-BI-LSTM) for flight delay prediction. The Bidirectional LSTM model extracts the spatial and temporal of the flight network with weather features. The 'Attention mechanism' has been proposed to enable the model to discover significant and discriminating features that contribute to categorization. The first stage of the proposed framework is the 'preprocessing of dataset' which is performed through two steps. The first step is data transformation using MinMax scaler to reduce the variation in the data. The second step is 'balancing the dataset' using SMOTE technique for balancing data. The second stage is the establishment of the ATT-BI-LSTM network through the deep tuning experiments of network structure to identify the best combination of parameters and network architecture. To validate the performance of the proposed framework, a wide network of US domestic flights tested in two scenarios. In Scenario 1, the objective is to predict the delay of flight arrival and departure, by using basic flight features with the weather. In Scenario 2, the objective is to predict the delay of flight arrival, by using basic flight features with departure delays. Simulation results show that in scenario 1, the training accuracy of flights' delay is 88% in both flight delay arrivals and departure, and the testing accuracies of flights' delay 83% and 82% in departure and arrival respectively. On the other hand, in scenario 2, testing accuracies are 94.30% and 93.71% in the two datasets respectively. The simulation results show that the ATT-BI-LSTM model outperforms other models found in the literature. Therefore, the developed ATT-BI-LSTM framework can contribute strongly to mitigating flight delays by providing a high accuracy prediction system in real-time monitoring to airport and aviation authorities. 

Improving flight delays prediction by developing attention-based bidirectional LSTM network

Nowadays, the civil aviation industry has a high precision demand of flight delay prediction. To make full use of the characteristics of flight data and meteorological data, two flight delay prediction models using deep convolution neural network based on fusion of meteorological data are proposed in this paper. One is DCNN (Dual-channel Convolutional Neural Network), which refers to the ResNet network structure. The other is SE-DenseNet (Squeeze and Excitation-Densely Connected Convolutional Network), combining the advantages of DenseNet and SENet. Firstly, flight data and meteorological data are fused in the model. Then, both DCNN and SE-DenseNet models are used to extract feature automatically based on the fused flight data set. Finally, the softmax classifier is adopted to predict the flight delay level. For proposed DCNN model, both straight channel and convolution channel are designed to guarantee the lossless transmission of the feature matrix and enhance the patency of the deep network. For proposed SE-DenseNet model, a SE module is added after the convolution layer of each DenseNet block, which can not only enhance the transmission of deep information but also achieve feature recalibration in the feature extraction process. The research results indicate that after considering characteristics of meteorological information, the accuracy of the model can be improved 1% compared with only considering the flight information. The two deep convolutional neural networks proposed in this paper, DCNN and SE-DenseNet, can both effectively improve the prediction accuracies, reaching to 92.1% and 93.19%, respectively.

Flight Delay Prediction Using Deep Convolutional Neural Network Based on Fusion of Meteorological Data

Quality assessment of tree-like structures obtained from a neuron reconstruction algorithm is necessary for evaluating the performance of the algorithm. The lack of user-friendly software for calculating common metrics motivated us to develop a Python toolbox called PyNeval, which is the first open-source toolbox designed to evaluate reconstruction results conveniently as far as we know. The toolbox supports popular metrics in two major categories, geometrical metrics and topological metrics, with an easy way to configure custom parameters for each metric. We tested the toolbox on both synthetic data and real data to show its reliability and robustness. As a demonstration of the toolbox in real applications, we used the toolbox to improve the performance of a tracing algorithm successfully by integrating it into an optimization procedure.

/pdf/pyneval-a-python-toolbox-for-evaluating-neuron-2d091wmv.pdf

PyNeval: A Python Toolbox for Evaluating Neuron Reconstruction Performance

Research on the supply risk propagation in the global iron ore trade network

Lithium gains an increasing importance in new energy vehicles and stationary energy storage, and development and utilization of lithium mines has attracted great attention around the world. In addition to the traditional lithium resources, lithium resources in coal seams have great potential in industrial application. Therefore, how to develop and utilize them is strategically significant for guaranteeing supply of lithium resources in China and promoting clean energy transformation. This paper summarizes the metallogenic ages, occurrence, enrichment factors, availability of lithium resources in coal seams in China. Conclusion are obtained as follows. i. coal-hosted lithium deposits discovered in China so far mainly occur in the Carboniferous-Permian strata of North China. They are less concentrated in smaller range in the Permian strata of Qilian-Qinling. ii. In China, coal-hosted lithium is mainly enriched in aluminum-bearing minerals. Lithium in coal seams mainly occurs in the inorganic matter, and some occurs in the organic matter. iii. Lithium enrichment in coal seams is caused by stable supply of aluminum and lithium-bearing minerals and special structural and geological factors. iv. According to China’s national standards on grades of lithium content in coal seams, the metallogenic belts of lithium in coal seams at the southern foothill of Yin Shan and in Qilian-Qinling have the potential of lithium development and extraction, covering Junger mining area in Inner Mongolia, and Pingshuo and Ningwu mining areas in Shanxi.

A review of China’s resources of lithium in coal seams

Curvilinear structures, which include line-like continuous objects, are fundamental geometrical elements in image-based applications. Reconstructing these structures from images constitutes a pivotal research area in computer vision. However, the complex topology and ambiguous image evidence render this process a challenging task. In this paper, we introduce DeepBranchTracer, a novel method that learns both external image features and internal geometric characteristics to reconstruct curvilinear structures. Firstly, we formulate the curvilinear structures extraction as a geometric attribute estimation problem. Then, a curvilinear structure feature learning network is designed to extract essential branch attributes, including the image features of centerline and boundary, and the geometric features of direction and radius. Finally, utilizing a multi-feature fusion tracing strategy, our model iteratively traces the entire branch by integrating the extracted image and geometric features. We extensively evaluated our model on both 2D and 3D datasets, demonstrating its superior performance over existing segmentation and reconstruction methods in terms of accuracy and continuity.

pdf/deepbranchtracer-a-generally-applicable-approach-to-58lykiiso4.pdf

DeepBranchTracer: A Generally-Applicable Approach to Curvilinear Structure Reconstruction Using Multi-Feature Learning

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