Enhancing Emergency Decision-making with Knowledge Graphs and Large Language Models

Emergency management urgently requires comprehensive knowledge while having a high possibility to go beyond individuals' cognitive scope. Therefore, artificial intelligence(AI) supported decision-making under that circumstance is of vital importance. Recent emerging large language models (LLM) provide a new direction for enhancing targeted machine intelligence. However, the utilization of LLM directly would inevitably introduce unreliable output for its inherent issue of hallucination and poor reasoning skills. In this work, we develop a system called Enhancing Emergency decision-making with Knowledge Graph and LLM (E-KELL), which provides evidence-based decision-making in various emergency stages. The study constructs a structured emergency knowledge graph and guides LLMs to reason over it via a prompt chain. In real-world evaluations, E-KELL receives scores of 9.06, 9.09, 9.03, and 9.09 in comprehensibility, accuracy, conciseness, and instructiveness from a group of emergency commanders and firefighters, demonstrating a significant improvement across various situations compared to baseline models. This work introduces a novel approach to providing reliable emergency decision support.Comment: 26 pages, 6 figure

DESIGN AND DEVELOPMENT OF A SMART ADVISORY SYSTEM FOR HAZARDOUS MATERIALS TRANSPORTATION RISK ANALYSIS VIA QUANTITATIVE APPROACHES

Safe transportation of hazardous materials is critical as it has a high potential of catastrophic accidents depending on the amount of transported product, its hazardous characteristics and the environmental conditions. Consequently, an efficient, smart and reliable intervention is essential to enhance prediction on the impacts of transportation hazards. Although various risk assessment techniques have been used in industry and regulatory bodies, they were developed for evaluating risk of hazardous materials for fixed installation cases instead of moving risk sources. This study applies the Transportation Risk Analysis (TRA), which is an extension of a well-known Quantitative Risk Analysis (QRA) technique in developing and design a Smart Advisory Systems (SAS), to determine the safest routes for transportation of hazardous materials according to Malaysia scenario

Analysis of the risks related to the logistics of the Hazardous Materials

Today, the number of industrial enterprises producing, using, storing and transporting hazardous materials is constantly increasing worldwide. This growth is linked to the progressive demand in various sectors, which makes our world riskier because of the nature and diversity of the dangerous events that may occur. The risks incurred by the hazardous materials transport activity, in case of the occurrence of an incident that may occur and have serious consequences for persons, the environment, property, a fire as an example accompanied by a release of toxic smoke, pollution of the soil and / or water, it can lead in case of non-control of the fire or the reactivity of the goods transported to an explosion. To this purpose, it is essential to protect the health and safety of personnel and to preserve the environment from any deterioration related to the risks incurred by the Transport of Dangerous Goods (TDG) business, which presents important issues for population, state and highly urbanized areas The aim of this thesis is to propose a systemic approach to risk assessment, taking into account in a global way the risks related to hazardous materials throughout the logistics chain (transport & storage). The approach consists of using the modeling and simulation techniques of an accident, to understand the consequences generated in the various scenarios in the event of the occurrence of a hazardous materials accident. This approach will allow the presentation of an industrial safety reasoning method based on actual case studies, rather than a detailed analysis of how to prevent and protect a given hazard. In the process of assessing the technological risks associated with the Transport of Dangerous Goods (TDG), the essential step is the evaluation of the risk intensity when an accidental event occurs, which is to quantify the risks involved. effects or impacts, in order to respond quickly and prioritize relief actions for the protection of the population and the environment. The assessment of the intensity of a technological risk can be carried out using an effects model, capable of estimating the effects induced by the hazardous phenomenon from a quantitative point of view, in order to determine the geographical area of the hazard where the intensity of the risk is deemed too high. In this context, the first issue addressed in this thesis is to assess the level of risk of hazardous goods transport areas for both road and marine modes of transportation, while the second issue of assessing risks in an industrial facility fixed

Statistical Investigation of Road and Railway Hazardous Materials Transportation Safety

Transportation of hazardous materials (hazmat) in the United States (U.S.) constituted 22.8% of the total tonnage transported in 2012 with an estimated value of more than 2.3 billion dollars. As such, hazmat transportation is a significant economic activity in the U.S. However, hazmat transportation exposes people and environment to the infrequent but potentially severe consequences of incidents resulting in hazmat release. Trucks and trains carried 63.7% of the hazmat in the U.S. in 2012 and are the major foci of this dissertation. The main research objectives were 1) identification and quantification of the effects of different factors on occurrence and consequences of hazmat-related incidents, towards identifying effective policies and countermeasures for improving safety and; 2) quantifying components of risk of hazmat transportation for costs prediction, planning purposes, or short-term decision-making. A comprehensive review of literature, study framework, and available data led to identification of six foci for this dissertation: 1) estimation of hazmat release statistical models for railroad incidents; 2) estimation of rollover and hazmat release statistical models for Cargo Tank Truck (CTT) crashes; 3) analyzing hazmat-involved crashes at highway-rail grade crossings (HRGCs); 4) model-based and non-model-based methods for classifying hazmat release from trains and CTTs; 5) estimation of macroscopic-level statistical models for frequency and severity of rail-based crude oil release incidents; and 6) estimation of statistical models for types and consequences of rail-based crude oil release incidents. Some of the findings of this research include: train derailments increased hazmat release probability more than other incident types; non-collision CTT crashes were more likely to result in rollovers, while rolling over increased the likelihood of hazmat release; at HRGCs, flashing signal lights were associated with lower hazmat release probability from trucks; increase in volume and distance of crude oil shipped from one state to another led to greater frequency and severity of incidents between the two states; and in rail-based crude oil release incidents, non-accident releases were associated with higher probability of gas dispersion, and lower probability of fire and explosion. Based on the results, recommendations regarding policies and countermeasures for improving safety are provided. Advisor: Aemal Khatta

OPTIMIZATION OF RAILWAY TRANSPORTATION HAZMATS AND REGULAR COMMODITIES

Transportation of dangerous goods has been receiving more attention in the realm of academic and scientific research during the last few decades as countries have been increasingly becoming industrialized throughout the world, thereby making Hazmats an integral part of our life style. However, the number of scholarly articles in this field is not as many as those of other areas in SCM. Considering the low-probability-and-high-consequence (LPHC) essence of transportation of Hazmats, on the one hand, and immense volume of shipments accounting for more than hundred tons in North America and Europe, on the other, we can safely state that the number of scholarly articles and dissertations have not been proportional to the significance of the subject of interest. On this ground, we conducted our research to contribute towards further developing the domain of Hazmats transportation, and sustainable supply chain management (SSCM), in general terms. Transportation of Hazmats, from logistical standpoint, may include all modes of transport via air, marine, road and rail, as well as intermodal transportation systems. Although road shipment is predominant in most of the literature, railway transportation of Hazmats has proven to be a potentially significant means of transporting dangerous goods with respect to both economies of scale and risk of transportation; these factors, have not just given rise to more thoroughly investigation of intermodal transportation of Hazmats using road and rail networks, but has encouraged the competition between rail and road companies which may indeed have some inherent advantages compared to the other medium due to their infrastructural and technological backgrounds. Truck shipment has ostensibly proven to be providing more flexibility; trains, per contra, provide more reliability in terms of transport risk for conveying Hazmats in bulks. In this thesis, in consonance with the aforementioned motivation, we provide an introduction into the hazardous commodities shipment through rail network in the first chapter of the thesis. Providing relevant statistics on the volume of Hazmat goods, number of accidents, rate of incidents, and rate of fatalities and injuries due to the incidents involving Hazmats, will shed light onto the significance of the topic under study. As well, we review the most pertinent articles while putting more emphasis on the state-of-the-art papers, in chapter two. Following the discussion in chapter 3 and looking at the problem from carrier company’s perspective, a mixed integer quadratically constraint problem (MIQCP) is developed which seeks for the minimization of transportation cost under a set of constraints including those associating with Hazmats. Due to the complexity of the problem, the risk function has been piecewise linearized using a set of auxiliary variables, thereby resulting in an MIP problem. Further, considering the interests of both carrier companies and regulatory agencies, which are minimization of cost and risk, respectively, a multiobjective MINLP model is developed, which has been reduced to an MILP through piecewise linearization of the risk term in the objective function. For both single-objective and multiobjective formulations, model variants with bifurcated and nonbifurcated flows have been presented. Then, in chapter 4, we carry out experiments considering two main cases where the first case presents smaller instances of the problem and the second case focuses on a larger instance of the problem. Eventually, in chapter five, we conclude the dissertation with a summary of the overall discussion as well as presenting some comments on avenues of future work

Deep learning for the early detection of harmful algal blooms and improving water quality monitoring

Climate change will affect how water sources are managed and monitored. The frequency of algal blooms will increase with climate change as it presents favourable conditions for the reproduction of phytoplankton. During monitoring, possible sensory failures in monitoring systems result in partially filled data which may affect critical systems. Therefore, imputation becomes necessary to decrease error and increase data quality. This work investigates two issues in water quality data analysis: improving data quality and anomaly detection. It consists of three main topics: data imputation, early algal bloom detection using in-situ data and early algal bloom detection using multiple modalities.The data imputation problem is addressed by experimenting with various methods with a water quality dataset that includes four locations around the North Sea and the Irish Sea with different characteristics and high miss rates, testing model generalisability. A novel neural network architecture with self-attention is proposed in which imputation is done in a single pass, reducing execution time. The self-attention components increase the interpretability of the imputation process at each stage of the network, providing knowledge to domain experts.After data curation, algal activity is predicted using transformer networks, between 1 to 7 days ahead, and the importance of the input with regard to the output of the prediction model is explained using SHAP, aiming to explain model behaviour to domain experts which is overlooked in previous approaches. The prediction model improves bloom detection performance by 5% on average and the explanation summarizes the complex structure of the model to input-output relationships. Performance improvements on the initial unimodal bloom detection model are made by incorporating multiple modalities into the detection process which were only used for validation purposes previously. The problem of missing data is also tackled by using coordinated representations, replacing low quality in-situ data with satellite data and vice versa, instead of imputation which may result in biased results

Coordinated Transit Response Planning and Operations Support Tools for Mitigating Impacts of All-Hazard Emergency Events

This report summarizes current computer simulation capabilities and the availability of near-real-time data sources allowing for a novel approach of analyzing and determining optimized responses during disruptions of complex multi-agency transit system. The authors integrated a number of technologies and data sources to detect disruptive transit system performance issues, analyze the impact on overall system-wide performance, and statistically apply the likely traveler choices and responses. The analysis of unaffected transit resources and the provision of temporary resources are then analyzed and optimized to minimize overall impact of the initiating event

A Methodology to Develop a Decision Model Using a Large Categorical Database with Application to Identifying Critical Variables during a Transport-Related Hazardous Materials Release

An important problem in the use of large categorical databases is extracting information to make decisions, including identification of critical variables. Due to the complexity of a dataset containing many records, variables, and categories, a methodology for simplification and measurement of associations is needed to build the decision model. To this end, the proposed methodology uses existing methods for categorical exploratory analysis. Specifically, latent class analysis and loglinear modeling, which together constitute a three-step, non-simultaneous approach, were used to simplify the variables and measure their associations, respectively. This methodology has not been used to extract data-driven decision models from large categorical databases. A case in point is a large categorical database at the DoT for hazardous materials releases during transportation. This dataset is important due to the risk from an unintentional release. However, due to the lack of a data-congruent decision model of a hazmat release, current decision making, including critical variable identification, is limited at the Office of Hazardous Materials within the DoT. This gap in modeling of a release is paralleled by a similar gap in the hazmat transportation literature. The literature has an operations research and quantitative risk assessment focus, in which the models consist of simple risk equations or more complex, theoretical equations. Thus, based on critical opportunities at the DoT and gaps in the literature, the proposed methodology was demonstrated using the hazmat release database. The methodology can be applied to other categorical databases for extracting decision models, such as those at the National Center for Health Statistics. A key goal of the decision model, a Bayesian network, was identification of the most influential variables relative to two consequences or measures of risk in a hazmat release, dollar loss and release quantity. The most influential variables for dollar loss were found to be variables related to container failure, specifically the causing object and item-area of failure on the container. Similarly, for release quantity, the container failure variables were also most influential, specifically the contributing action and failure mode. In addition, potential changes in these variables for reducing consequences were identified

Three Essays on Strategic Behavior and Policy.

This dissertation contains three chapters that study the strategic behavior of economic agents in environments shaped by legal and regulatory policy. Two chapters examine strategic behavior as it pertains to the market outcomes of firms competing as oligopolists; a third studies the negotiation outcomes of a firm and consumer bargaining in the shadow of the law. Each chapter extends or applies fundamental models of strategic behavior to develop testable implications. It then exploits the historical change induced by a policy intervention to empirically test the model's mechanisms, conditions, and predictions. Finally, it interprets the meaning of empirical estimates for policy toward antitrust and other types of law. The first chapter analyzes the sustainability and consequencs of collusion in a high-technology industry with learning-by-doing. It shows how the dynamic cost savings that occur through learning, combined with multiproduct competition, can reverse the standard predictions of static supergame models of collusion. It illustrates this by building an oligopolistic model of learning-by-doing embedded within a supergame. It estimates the price change attributable to collusion by using firm-level data before, during, and after explicit collusion in the electronic memory chip market. The second chapter assesses the predictions of the Priest-Klein model of pre-trial settlement bargaining. It applies the model to an auto insurance negotiation setting to show how parties' uncertainty in a legal standard drives the likelihood of claim settlement. It then exploits state- and time-level variation in states' adoption of tort liability for insurance bad faith law to find that tort liability significantly increased the likelihood of a claim to settle with litigation in the short-term, but significantly reduced this likelihood in the long-term. The third chapter uses data from a price fixing cartel in the Korean petrochemical industry to test empirical screens of collusion. It exploits the information disclosed by the Korean antitrust regulator to formulate a hypothesis of the effect of collusion on price-cost asymmetry. It uses price data before and after the breakdown of the cartel to find evidence consistent with a change from high to low price-cost asymmetry before and after the cartel's collapse.PhDBusiness AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113443/1/dasmat_1.pd

Applied Cognitive Sciences

Cognitive science is an interdisciplinary field in the study of the mind and intelligence. The term cognition refers to a variety of mental processes, including perception, problem solving, learning, decision making, language use, and emotional experience. The basis of the cognitive sciences is the contribution of philosophy and computing to the study of cognition. Computing is very important in the study of cognition because computer-aided research helps to develop mental processes, and computers are used to test scientific hypotheses about mental organization and functioning. This book provides a platform for reviewing these disciplines and presenting cognitive research as a separate discipline