Advances in Deep Learning Towards Fire Emergency Application : Novel Architectures, Techniques and Applications of Neural Networks

Paper IV is not published yet.With respect to copyright paper IV and paper VI was excluded from the dissertation.Deep Learning has been successfully used in various applications, and recently, there has been an increasing interest in applying deep learning in emergency management. However, there are still many significant challenges that limit the use of deep learning in the latter application domain. In this thesis, we address some of these challenges and propose novel deep learning methods and architectures. The challenges we address fall in these three areas of emergency management: Detection of the emergency (fire), Analysis of the situation without human intervention and finally Evacuation Planning. In this thesis, we have used computer vision tasks of image classification and semantic segmentation, as well as sound recognition, for detection and analysis. For evacuation planning, we have used deep reinforcement learning.publishedVersio

Towards predicting Pedestrian Evacuation Time and Density from Floorplans using a Vision Transformer

Conventional pedestrian simulators are inevitable tools in the design process of a building, as they enable project engineers to prevent overcrowding situations and plan escape routes for evacuation. However, simulation runtime and the multiple cumbersome steps in generating simulation results are potential bottlenecks during the building design process. Data-driven approaches have demonstrated their capability to outperform conventional methods in speed while delivering similar or even better results across many disciplines. In this work, we present a deep learning-based approach based on a Vision Transformer to predict density heatmaps over time and total evacuation time from a given floorplan. Specifically, due to limited availability of public datasets, we implement a parametric data generation pipeline including a conventional simulator. This enables us to build a large synthetic dataset that we use to train our architecture. Furthermore, we seamlessly integrate our model into a BIM-authoring tool to generate simulation results instantly and automatically

Location-allocation models for relief distribution and victim evacuation after a sudden-onset natural disaster

Quick response to natural disasters is vital to reduce loss of and negative impact to human life. The response is more crucial in the presence of sudden-onset, difficult-to-predict natural disasters, especially in the early period of those events. On-site actions are part of such response, some of which are determination of temporary shelters and/ or temporary medical facility locations, the evacuation process of victims and relief distribution to victims. These activities of last-mile disaster logistics are important as they are directly associated with sufferers, the main focus of any alleviation of losses caused by any disaster. This research deals with the last-mile site positioning of relief supplies and medical facilities in response to a sudden-onset, difficult-to-predict disaster event, both dynamically and in a more coordinative way during a particular planning time horizon. Four mathematical models which reflect the situation in Padang Pariaman District after the West Sumatera earthquake were built and tested. The models are all concerned with making decisions in a rolling time horizon manner, but differ in coordinating the operations and in utilization of information about future resource availability. Model I is a basic model representing the current practice with relief distribution and victim evacuation performed separately and decisions made only considering the resources available at the time. Model II considers coordination between the two operations and conducts them with the same means of transport. Model III takes into account future information keeping the two operations separate. Model IV combines the features of Models II and III. The four models are approached both directly and by using various heuristics. The research shows that conducting relief distribution and victim evacuation activities by using shared vehicles and/or by taking into account future information on resource availability improves the current practice . This is clearly demonstrated by the experimental results on small problems. For large problems, experiments show that it is not practical to directly solve the models, especially the last three, and that the solution quality is poor when the solution process is limited to a reasonable time. Experiments also show that the heuristics help improve the solution quality and that the performances of the heuristics are different for different models. When each model is solved using its own best heuristic, the conclusions from results of large problems get very close to those from small problems. Finally, deviation of future information on resource availability is considered in the study, but is shown not to affect the performance of model III and model IV in carrying out relief distribution and victim evacuation. This indicates that it is always worthwhile to take into account the future information, even if the information is not perfect, as long as it is reasonably reliable

Private and Public Responses to Climate Shocks

human development, climate change

Climate Change and Risk Management: Challenges for Insurance, Adaptation, and Loss Estimation

Adapting to climate change will not only require responding to the physical effects of global warming, but will also require adapting the way we conceptualize, measure, and manage risks. Climate change is creating new risks, altering the risks we already face, and also, importantly, impacting the interdependencies between these risks. In this paper we focus on three particular phenomena of climate related risks that will require a change in our thinking about risk management: global micro-correlations, fat tails, and tail dependence. Consideration of these phenomena will be particularly important for natural disaster insurance, as they call into question traditional methods of securitization and diversification.tail dependence, micro-correlations, fat tails, damage distributions, climate change

Emergency response in complex buildings: Automated selection of safest and balanced routes

The extreme importance of emergency response in complex buildings during natural and human-induced disasters has been widely acknowledged. In particular, there is a need for efficient algorithms for finding safest evacuation routes, which would take into account the 3-D structure of buildings, their relevant semantics, and the nature and shape of hazards. In this article, we propose algorithms for safest routes and balanced routes in buildings, where an extreme event with many epicenters is occurring. In a balanced route, a trade-off between route length and hazard proximity is made. The algorithms are based on a novel approach that integrates a multiattribute decision-making technique, Dijkstra's classical algorithm and the introduced hazard proximity numbers, hazard propagation coefficient and proximity index for a route

Avoiding Fire in the Operating Suite: An Intersection of Prevention and Common Sense

The operating room (OR) is a complex environment that involves large teams and multiple competing priorities, dynamically interacting throughout the entire course of a surgical procedure. The simultaneous presence of flammable substances, volatile gases, and the frequent use of electrical current results in a potentially dangerous combination. Operating room fire (ORF) is a rare but potentially devastating occurrence. To prevent this “never event”, it is critical for institutions to establish and follow proper fire safety protocols. Adherence to proven prevention strategies and awareness of associated risk factors will help reduce the incidence of this dreaded safety event. When ORF does occur despite strict adherence to established safety protocols, the entire OR team should know the steps required to contain and extinguish the fire as well as essential measures to minimize or avoid thermal injury. If injury does occur, it is important to recognize and treat it promptly. Appropriate and honest disclosure to all injured persons and their families should be made without delay. As with all serious patient safety events, regulatory reporting and root cause determinations must take place in accordance with applicable laws and regulations. The goal of patient safety champions at each institution should be the attainment of zero incidence of ORF

The Relative Effects of Logistics, Coordination and Human Resource on Humanitarian Aid and Disaster Relief Mission Performance

Most studies on humanitarian aid and disaster relief (HADR) missions suggest that the quality of logistics, coordination and human resource management will affect their performance. However, studies in developing countries are mainly conceptual and lack the necessary empirical evidence to support these contentions. The current paper thereby aimed to fill this knowledge gap by statistically examining the effects of the abovementioned factors on such missions. Focusing on the Malaysian army due to its extensive experience in HADR operations, the paper opted for a quantitative approach to allow for a more objective analysis of the issues. The results show that there are other potential determinants of mission success which deserve due attention in future studies. They also suggest that human resource is not easily measured as a construct, and that this limitation in methodology must be overcome to derive more accurate conclusions regarding its effect on HADR mission performance.&nbsp

Precursor Analysis for Offshore Oil and Gas Drilling: From Prescriptive to Risk-Informed Regulation

The Oil Spill Commission’s chartered mission—to “develop options to guard against … any oil spills associated with offshore drilling in the future” (National Commission 2010)—presents a major challenge: how to reduce the risk of low-frequency oil spill events, and especially high-consequence events like the Deepwater Horizon accident, when historical experience contains few oil spills of material scale and none approaching the significance of the Deepwater Horizon. In this paper, we consider precursor analysis as an answer to this challenge, addressing first its development and use in nuclear reactor regulation and then its applicability to offshore oil and gas drilling. We find that the nature of offshore drilling risks, the operating information obtainable by the regulator, and the learning curve provided by 30 years of nuclear experience make precursor analysis a promising option available to the U.S. Bureau of Ocean Energy Management, Regulation and Enforcement (BOEMRE) to bring cost-effective, risk-informed oversight to bear on the threat of catastrophic oil spills.catastrophic oil spills, quantitative risk analysis, risk-informed regulation