Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph Learning

Air traffic control (ATC) is a safety-critical service system that demands constant attention from ground air traffic controllers (ATCos) to maintain daily aviation operations. The workload of the ATCos can have negative effects on operational safety and airspace usage. To avoid overloading and ensure an acceptable workload level for the ATCos, it is important to predict the ATCos' workload accurately for mitigation actions. In this paper, we first perform a review of research on ATCo workload, mostly from the air traffic perspective. Then, we briefly introduce the setup of the human-in-the-loop (HITL) simulations with retired ATCos, where the air traffic data and workload labels are obtained. The simulations are conducted under three Phoenix approach scenarios while the human ATCos are requested to self-evaluate their workload ratings (i.e., low-1 to high-7). Preliminary data analysis is conducted. Next, we propose a graph-based deep-learning framework with conformal prediction to identify the ATCo workload levels. The number of aircraft under the controller's control varies both spatially and temporally, resulting in dynamically evolving graphs. The experiment results suggest that (a) besides the traffic density feature, the traffic conflict feature contributes to the workload prediction capabilities (i.e., minimum horizontal/vertical separation distance); (b) directly learning from the spatiotemporal graph layout of airspace with graph neural network can achieve higher prediction accuracy, compare to hand-crafted traffic complexity features; (c) conformal prediction is a valuable tool to further boost model prediction accuracy, resulting a range of predicted workload labels. The code used is available at \href{https://github.com/ymlasu/para-atm-collection/blob/master/air-traffic-prediction/ATC-Workload-Prediction/}{$\mathsf{Link}$}

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system. Applications on low voltage, local networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known low voltage level open datasets to encourage further research and development.Comment: 37 pages, 6 figures, 2 tables, review pape

Climate variability and trends in the Valkea-Kotinen region, southern Finland : comparisons between the past, current and projected climates

Peer reviewe

Climatic Constraints and Human Activities; Proceedings of a Task Force on the Nature of Climate and Society Research, February 4-6, 1980

This book contains a summary essay and seven invited papers from the Task Force meeting on the Nature of Climate Society Research. The introductory essay examines the differences in research methods on questions of short-time climatic change, and identifies some important avenues for research. The first two papers, by Ausubel and Meyer-Abich, take broad looks at climate and public policy. Ausubel offers arguments from an economic point of view as to why the atmosphere is increasingly associated with developments, like climatic change, that are threatening to human activity. The paper by Meyer-Abich surveys from a political point of view the reasons that regulation of activities which could control or prevent climatic change are unlikely to take place, and why adaptation is the most likely path to be followed, especially given the current weakness of the interdisciplinary analysis of the problem of climatic change. The paper by Biswas narrows the focus and illuminates the uncertainty associated with one specific but very prominent area, the relationships between climate and crops, which one might easily assume otherwise to be a more secure area of knowledge. Three case study approaches follow, two emphasizing a geographical perspective and one a social group. Warrick's historical study of the possible "lessening" of drought impacts in the Great Plains of the USA emphasizes the need for a clear setting out of hypotheses to be tested in research on the relationship of climate and society and the need for improvements of the modelling of the overall system. Spitz develops a model of a food-producing class which is also self-provisioning, i.e., where food has a dual nature as both a basic need and as merchandise to be traded, and explores the significance of drought to such a group, with particular reference to Eastern India. Czelnai's paper on the Great Plain of the Danube Basin offers interesting insights into the extent to which natural systems have already been transformed by man and proposes ways in which sensitivity and vulnerability to climatic factors may be defined and explored. Finally, Sergin proposes a method of estimating plausible patterns of climatic change based on the similarity between seasonal changes and climatic changes of physical fields on longer time scales

Narrative Leadership: Exploring the Concept of Time in Leader Storytelling

This dissertation explores leader storytelling and the use of temporality in leader enactment. Although narrative leadership is broadly described in previous theory as leading with storytelling, a formal theory of narrative leadership has not yet been developed. Recently, researchers have identified the narrator’s ability to locate a story within a meaningful time continuum of past, present, and future as potentially important. Using a grounded theory approach, the question that guided the research was: How does the use of time in narrative impact the enactment of leadership during a strategic change? With the goal of developing a theory that emerges from the ground up, a three-pronged approach was utilized. A review of the literature on narrative, leader sensegiving and sensemaking, and current conceptualizations of temporality (including cosmic versus phenomenological time; chronos, kairos, and chaos; monochronic, polychronic, and cyclical orientation; and near-distant-deep time) was conducted. Then, seven leaders identified as exemplars in the use of storytelling for organizational change were interviewed. These interviews were coded and analyzed for emergent concepts to build a theoretical model of story and time. The model was assessed with the reflections of employees of a sub-set of the original leaders and researchers’ reflexive journals. The process model of time-based narrative leadership that culminated from these steps includes three critical components: action, identity, and meaning. Action refers to the new or changed cognition or behavior that the leader’s story prompts; identity is the centrality of the leader’s past experience for facilitating listener engagement and visualizing a landscape for future action; and meaning is the leader’s sensemaking for understanding and learning at personal or collective levels. Furthermore, it is proposed that the theory of sensegiving provides the best framing for the observed stories, and that the study’s culminating model contributes important directions for future research. In the leaders’ stories, giving sense to others in the organization pivots on the leaders’ own personal experience as landscape for the unknown future. Implications of the culminating model and directions for future research are discussed

Machine Learning Applications for Load Predictions in Electrical Energy Network

In this work collected operational data of typical urban and rural energy network are analysed for predictions of energy consumption, as well as for selected region of Nordpool electricity markets. The regression techniques are systematically investigated for electrical energy prediction and correlating other impacting parameters. The k-Nearest Neighbour (kNN), Random Forest (RF) and Linear Regression (LR) are analysed and evaluated both by using continuous and vertical time approach. It is observed that for 30 minutes predictions the RF Regression has the best results, shown by a mean absolute percentage error (MAPE) in the range of 1-2 %. kNN show best results for the day-ahead forecasting with MAPE of 2.61 %. The presented vertical time approach outperforms the continuous time approach. To enhance pre-processing stage, refined techniques from the domain of statistics and time series are adopted in the modelling. Reducing the dimensionality through principal components analysis improves the predictive performance of Recurrent Neural Networks (RNN). In the case of Gated Recurrent Units (GRU) networks, the results for all the seasons are improved through principal components analysis (PCA). This work also considers abnormal operation due to various instances (e.g. random effect, intrusion, abnormal operation of smart devices, cyber-threats, etc.). In the results of kNN, iforest and Local Outlier Factor (LOF) on urban area data and from rural region data, it is observed that the anomaly detection for the scenarios are different. For the rural region, most of the anomalies are observed in the latter timeline of the data concentrated in the last year of the collected data. For the urban area data, the anomalies are spread out over the entire timeline. The frequency of detected anomalies where considerably higher for the rural area load demand than for the urban area load demand. Observing from considered case scenarios, the incidents of detected anomalies are more data driven, than exceptions in the algorithms. It is observed that from the domain knowledge of smart energy systems the LOF is able to detect observations that could not have detected by visual inspection alone, in contrast to kNN and iforest. Whereas kNN and iforest excludes an upper and lower bound, the LOF is density based and separates out anomalies amidst in the data. The capability that LOF has to identify anomalies amidst the data together with the deep domain knowledge is an advantage, when detecting anomalies in smart meter data. This work has shown that the instance based models can compete with models of higher complexity, yet some methods in preprocessing (such as circular coding) does not function for an instance based learner such as k-Nearest Neighbor, and hence kNN can not option for this kind of complexity even in the feature engineering of the model. It will be interesting for the future work of electrical load forecasting to develop solution that combines a high complexity in the feature engineering and have the explainability of instance based models.publishedVersio

1994 - Managing Water for Drought, U.S. Army Corps of Engineers and Institute for Water Resources

The purpose of the report was to explain the procedure for cooperative federal-state drought preparedness studies, to indicate how the studies related to the longstanding principles and guidance for federal water resources investigations, and to indicate the means of implementing conclusions arrived at in any given region. This 1994 report, developed during a four-year National Study of Water Management, summarizes the method of improving water management during drought. The method was tested and refined in four field studies in different parts of the country, in which teams of water managers and users worked together to reduce drought impacts. In each case, the situations were complex, involving may different uses of water. Because state and local responsibilities were involved, a joint cooperative approach between state and federal agencies was necessary to provide satisfactory answers.https://digitalcommons.csumb.edu/hornbeck_usa_2_f/1000/thumbnail.jp

Solar-terrestrial Predictions Proceedings. Volume 1: Prediction Group Reports

The current practice in solar terrestrial predictions is reviewed with emphasis of prediction, warning, and monitoring services. Topics covered include: ionosphere-reflected HF radio propagation; radiation hazards for manned space flights and high altitude and high latitude aircraft flights; and geomagnetic activity