Spatiotemporal Propagation Learning for Network-Wide Flight Delay Prediction

Demystifying the delay propagation mechanisms among multiple airports is fundamental to precise and interpretable delay prediction, which is crucial during decision-making for all aviation industry stakeholders. The principal challenge lies in effectively leveraging the spatiotemporal dependencies and exogenous factors related to the delay propagation. However, previous works only consider limited spatiotemporal patterns with few factors. To promote more comprehensive propagation modeling for delay prediction, we propose SpatioTemporal Propagation Network (STPN), a space-time separable graph convolutional network, which is novel in spatiotemporal dependency capturing. From the aspect of spatial relation modeling, we propose a multi-graph convolution model considering both geographic proximity and airline schedule. From the aspect of temporal dependency capturing, we propose a multi-head self-attentional mechanism that can be learned end-to-end and explicitly reason multiple kinds of temporal dependency of delay time series. We show that the joint spatial and temporal learning models yield a sum of the Kronecker product, which factors the spatiotemporal dependence into the sum of several spatial and temporal adjacency matrices. By this means, STPN allows cross-talk of spatial and temporal factors for modeling delay propagation. Furthermore, a squeeze and excitation module is added to each layer of STPN to boost meaningful spatiotemporal features. To this end, we apply STPN to multi-step ahead arrival and departure delay prediction in large-scale airport networks. To validate the effectiveness of our model, we experiment with two real-world delay datasets, including U.S and China flight delays; and we show that STPN outperforms state-of-the-art methods. In addition, counterfactuals produced by STPN show that it learns explainable delay propagation patterns.Comment: 14 pages,8 figure

Using Machine Learning to Predict Damaging Straight-line Convective Winds

Thunderstorms, including straight-line (non-tornadic) winds, cause an average of over 100 deaths and $10 billion of insured damage per year in the United States. In the past decade machine learning has led to significant improvements in the prediction of other convective hazards, such as tornadoes, hail, lightning, and convectively induced aircraft turbulence. However, very few studies have used machine learning specifically to predict damaging straight-line winds. We have developed machine-learning models to predict the probability of damaging straight-line wind, defined as a gust ≥ 50 kt (25.72 m s-1), for a given storm cell. Predictions are made for three buffer distances around the storm cell (0, 5, and 10 km) and five lead-time windows ([0, 15]; [15, 30]; [30, 45]; [45, 60]; and [60, 90] minutes). Three types of data are used to train models: radar images from the Multi-year Reanalysis of Remotely Sensed Storms (MYRORSS); atmospheric soundings from the Rapid Update Cycle (RUC) model and North American Regional Reanalysis (NARR); and near-surface wind observations from the Meteorological Assimilation Data Ingest System (MADIS), Oklahoma Mesonet, one-minute meteorological aerodrome reports (METARs), and National Weather Service local storm reports. Radar images are used to determine the structural and hydrometeorological properties of storm cells, while soundings are used to determine properties of the near-storm environment, which are important for storm evolution. Both of these data types are used to create predictor variables. Meanwhile, near-surface wind observations are used as verification data (to determine which storm cells produced damaging straight-line winds). For each buffer distance and lead-time window, we experiment with five machine-learning algorithms: logistic regression, logistic regression with an elastic net, feed-forward neural nets, random forests, and gradient-boosted tree (GBT) ensembles. Forecast probabilities from each model are calibrated with isotonic regression, which makes them more reliable. Forecasts are verified mainly with three numbers: area under the receiver-operating-characteristic curve (AUC), maximum critical success index (CSI), and Brier skill score (BSS). AUC and maximum CSI range from [0, 1], where 0 is the worst score and 1 is a perfect score. BSS ranges from (−∞, 1], where −∞ is the worst score; 1 is a perfect score; and > 0 means that the model is better than climatology. Models are ranked by AUC. The best model (for a buffer distance of 0 km and lead time of [15, 30] minutes) has an AUC of 0.996, maximum CSI of 0.99, and BSS of 0.88. The worst model (for a buffer distance of 10 km and lead time of [60, 90] minutes) has an AUC of 0.89, maximum CSI of 0.20, and BSS of 0.12. All models outperform climatology. Finally, for each buffer distance and lead-time window, we use three methods to select the most important predictor variables: sequential forward selection, J-measures, and decision trees

Storm-scale Ensemble-based Severe Weather Guidance: Development of an Object-based Verification Framework and Applications of Machine Learning

A goal of the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast (WoF) project is to provide rapidly updating probabilistic guidance to human forecasters for short-term (e.g., 0-3 h) severe weather forecasts. Several case studies have shown that experimental WoF systems (WoFS) can produce accurate short-term probabilistic guidance for hazards such as tornadoes, hail, and heavy rainfall. However, without an appropriate probabilistic verification method for WoFS-style forecasts (which provide guidance for individual thunderstorms), a robust evaluation of WoFS performance has been lacking. In this dissertation, I develop a novel object-based verification method for short-term, storm-scale probabilistic forecasts and apply it to WoFS probabilistic mesocyclone guidance and further adapted to evaluate machine learning-based calibrations of WoFS severe weather probabilistic guidance. The probabilistic mesocyclone guidance was generated by calculating grid-scale ensemble probabilities from WoFS forecasts of updraft helicity (UH) in layers 2—5 km (mid-level) and 0—2 km (low-level) above ground level (AGL) aggregated over 60-min periods. The resulting ensemble probability swaths are associated with individual thunderstorms and treated as objects. Each ensemble track object is assigned a single representative probability value. A mesocyclone probability object, conceptually, is a region bounded by the ensemble forecast envelope of a mesocyclone track for a thunderstorm over 1 hour. The mesocyclone probability objects were matched against rotation track objects in Multi-Radar Multi-Sensor data using the total interest score, but with the maximum displacement varied between 0, 9, 15, and 30 km. Forecast accuracy and reliability were assessed at four different forecast lead time periods: 0-60 min, 30-90 min, 60-120 min, and 90-150 min. In the 0-60 minute forecast period, the low-level UH probabilistic forecasts had a POD, FAR, and CSI of 0.46, 0.45, and 0.31, respectively, with a probability threshold of 22.2% (the threshold of maximum CSI). In the 90-150 minute forecast period, the POD and CSI dropped to 0.39 and 0.27 while FAR remained relatively unchanged. Forecast probabilities >60% over-predicted the likelihood of observed mesocyclones in the 0-60 min period; however, reliability improved when allowing larger maximum displacements for object matching and at longer lead times. To evaluate the ability of machine learning (ML) models to calibrate WoFS severe weather guidance, the probability object-based method was generalized for identifying any ensemble storm track (based on individual ensemble updraft tracks rather than mesocyclone tracks). Using these ensemble storm tracks, three sets of predictors were extracted from the WoFS forecasts: intra-storm state variables, near-storm environment variables, and morphological attributes of the ensemble storm tracks. Random forests, gradient-boosted trees, and logistic regression algorithms were then trained to predict which WoFS 30-min ensemble storm tracks will produce a tornado, severe hail, and/or severe wind report. To provide a baseline against which to test the ML models’ performance, I extracted the probability of mid-level UH exceeding a threshold (tuned per severe weather hazard) from each ensemble storm track. The three ML algorithms discriminated well for all three hazards and produced far more reliable probabilities than the UH-based predictions. Using state-of-the-art ML interpretability methods, I found that the ML models learned sound physical relationships and the appropriate responses to the ensemble statistics. Intra-storm predictors were found to be more important than environmental predictors for all three ML models, but environmental predictors made positive contributions to severe weather likelihood in situations where the WoFS fails to analyze ongoing convection. Overall, the results suggest that ML-based calibrations of dynamical ensemble output can improve short term, storm-scale severe weather probabilistic guidance

Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review

Atmospheric Extreme Events (EEs) cause severe damages to human societies and ecosystems. The frequency and intensity of EEs and other associated events are increasing in the current climate change and global warming risk. The accurate prediction, characterization, and attribution of atmospheric EEs is therefore a key research field, in which many groups are currently working by applying different methodologies and computational tools. Machine Learning (ML) methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric EEs. This paper reviews the ML algorithms applied to the analysis, characterization, prediction, and attribution of the most important atmospheric EEs. A summary of the most used ML techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. A number of examples is discussed and perspectives and outlooks on the field are drawn.Comment: 93 pages, 18 figures, under revie

Coupling Data Science Techniques and Numerical Weather Prediction Models for High-Impact Weather Prediction

Meteorologists have access to more model guidance and observations than ever before, but this additional information does not necessarily lead to better forecasts. New tools are needed to reduce the cognitive load on forecasters and to provide them with accurate, reliable consensus guidance. Techniques from the data science community, such as machine learning and image processing, have the potential to summarize and calibrate numerical weather prediction model output and to generate deterministic and probabilistic forecasts of high-impact weather. In this dissertation, I developed data-science-based approaches to improve the predictions of two high-impact weather domains: hail and solar irradiance. Both hail and solar irradiance produce large economic impacts, have non-Gaussian distributions of occurrence, are poorly observed, and are partially driven by processes too small to be resolved by numerical weather prediction models. Hail forecasts were produced with convection-allowing model output from the Center for Analysis and Prediction of Storms and National Center for Atmospheric Research ensembles. The machine learning hail forecasts were compared against storm surrogate variables and physics-based diagnostic models of hail size. Initial machine learning hail forecasts reduced size errors but struggled with predicting extreme events. By coupling the machine learning model to predicting hail size distributions and estimating the distribution parameters jointly, the machine learning methods were able to show skill and reliability in predicting both severe and significant hail. Machine learning model and data configurations for gridded solar irradiance forecasting were evaluated on two numerical modeling systems. The evaluation determined how machine learning model choice, closeness of fit to training data, training data aggregation, and interpolation method affected forecasts of clearness index at Oklahoma Mesonet sites not included in the training data. The choice of machine learning model, interpolation scheme, and loss function had the biggest impacts on performance. Errors tended to be lower at testing sites with sunnier weather and those that were closer to training sites. All of the machine learning methods produced reliable predictions but underestimated the frequency of cloudiness compared to observations

Low-latency, query-driven analytics over voluminous multidimensional, spatiotemporal datasets

2017 Summer.Includes bibliographical references.Ubiquitous data collection from sources such as remote sensing equipment, networked observational devices, location-based services, and sales tracking has led to the accumulation of voluminous datasets; IDC projects that by 2020 we will generate 40 zettabytes of data per year, while Gartner and ABI estimate 20-35 billion new devices will be connected to the Internet in the same time frame. The storage and processing requirements of these datasets far exceed the capabilities of modern computing hardware, which has led to the development of distributed storage frameworks that can scale out by assimilating more computing resources as necessary. While challenging in its own right, storing and managing voluminous datasets is only the precursor to a broader field of study: extracting knowledge, insights, and relationships from the underlying datasets. The basic building block of this knowledge discovery process is analytic queries, encompassing both query instrumentation and evaluation. This dissertation is centered around query-driven exploratory and predictive analytics over voluminous, multidimensional datasets. Both of these types of analysis represent a higher-level abstraction over classical query models; rather than indexing every discrete value for subsequent retrieval, our framework autonomously learns the relationships and interactions between dimensions in the dataset (including time series and geospatial aspects), and makes the information readily available to users. This functionality includes statistical synopses, correlation analysis, hypothesis testing, probabilistic structures, and predictive models that not only enable the discovery of nuanced relationships between dimensions, but also allow future events and trends to be predicted. This requires specialized data structures and partitioning algorithms, along with adaptive reductions in the search space and management of the inherent trade-off between timeliness and accuracy. The algorithms presented in this dissertation were evaluated empirically on real-world geospatial time-series datasets in a production environment, and are broadly applicable across other storage frameworks

Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of additional travel time and increased fuel consumption. Integrating emerging technologies into transportation systems provides opportunities for improving traffic prediction significantly and brings about new research problems. In order to lay the foundation for understanding the open research challenges in traffic prediction, this survey aims to provide a comprehensive overview of traffic prediction methodologies. Specifically, we focus on the recent advances and emerging research opportunities in Artificial Intelligence (AI)-based traffic prediction methods, due to their recent success and potential in traffic prediction, with an emphasis on multivariate traffic time series modeling. We first provide a list and explanation of the various data types and resources used in the literature. Next, the essential data preprocessing methods within the traffic prediction context are categorized, and the prediction methods and applications are subsequently summarized. Lastly, we present primary research challenges in traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies (TR_C), Volume 145, 202

Effects of environmental shear and buoyancy on simulated supercell interactions

This study examines the role that buoyancy and vertical wind shear play in modulating the relationship between storm interactions and storm severity. Using an idealized numerical model, 240 supercell interactions are simulated under systematically varied amounts of buoyancy and vertical wind shear. Small changes in buoyancy or vertical wind shear have signi cant impacts on post-interaction storm morphology. A wide amount of variation in low-level rotation is seen across the simulation suite. Two-cell storm simulations are not always stronger than one-cell control simulations. Migration of low-level vertical vorticity centers is ubiquitous through all runs, but orientation of and interaction between two storms' gust fronts modulates where the vortic- ity center will end up. Gust fronts in better alignment have more vorticity centers reach an updraft where they are stretched and intensi ed. With re- spect to storm mode, higher buoyancy produced less classic supercells while higher shear produced more classic supercells. High precipitation supercells were favored with two-cell simulations where the second cell was directly to the southwest of the control cell. Secondary cells that were close to the con- trol merged quickly and were often stronger than simulations with large cell separation distance. Further questions remain with trajectories and machine learning algorithms are tthe next steps for a more detailed analysis of this large data set

WAZE Data Reporting

This study evaluated the quality of crowdsourced Waze data (including reports and speed) and explored promising use scenarios of Waze data to facilitate the development of intelligent transportation in Tennessee. To this end, the thoroughly assessed Waze reports quality in terms of spatiotemporal accuracy and coverage. The study found Waze users reported crash events about 2.2 minutes sooner, on average, than reports of the same events recorded in the state\u2019s Locate/IM incident log. The reported crash locations per Waze are on average 6 feet from the Locate/IM log reported by the officials. It is found that 26% of crashes reported in Waze was matched with 67% Locate/IM crash reports, with the rest 74% reports pointing to unreported incidents. Waze speed is affected by the Wazers behaviors and tends to be slightly higher than detector speed in free-flow status. This study evaluated several novel use scenarios such as secondary crash detection, end of queue detection and tracking, level of service evaluation, work zone monitoring, wildlife hazards and crashes, and pothole detection and maintenance. Results show that Waze is a suitable data source for incident management, level of service evaluation, work zone management, roadway maintenance management, etc. when properly used and in cooperation with the agency\u2019s other information sources