Artificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs

Artificial intelligence (AI) methods have seen increasingly widespread use in everything from consumer products and driverless cars to fraud detection and weather forecasting. The use of AI has transformed many of these application domains. There are ongoing efforts at leveraging AI for disaster risk analysis. This article takes a critical look at the use of AI for disaster risk analysis. What is the potential? How is the use of AI in this field different from its use in nondisaster fields? What challenges need to be overcome for this potential to be realized? And, what are the potential pitfalls of an AI‐based approach for disaster risk analysis that we as a society must be cautious of?Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155885/1/risa13476_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155885/2/risa13476.pd

Topological Performance Measures as Surrogates for Physical Flow Models for Risk and Vulnerability Analysis for Electric Power Systems

Critical infrastructure systems must be both robust and resilient in order to ensure the functioning of society. To improve the performance of such systems, we often use risk and vulnerability analysis to find and address system weaknesses. A critical component of such analyses is the ability to accurately determine the negative consequences of various types of failures in the system. Numerous mathematical and simulation models exist which can be used to this end. However, there are relatively few studies comparing the implications of using different modeling approaches in the context of comprehensive risk analysis of critical infrastructures. Thus in this paper, we suggest a classification of these models, which span from simple topologically-oriented models to advanced physical flow-based models. Here, we focus on electric power systems and present a study aimed at understanding the tradeoffs between simplicity and fidelity in models used in the context of risk analysis. Specifically, the purpose of this paper is to compare performances measures achieved with a spectrum of approaches typically used for risk and vulnerability analysis of electric power systems and evaluate if more simplified topological measures can be combined using statistical methods to be used as a surrogate for physical flow models. The results of our work provide guidance as to appropriate models or combination of models to use when analyzing large-scale critical infrastructure systems, where simulation times quickly become insurmountable when using more advanced models, severely limiting the extent of analyses that can be performed

Why are You Here? Modeling Illicit Massage Business Location Characteristics with Machine Learning

Illicit massage businesses are a venue for sex and labor trafficking in the United States. Though many of their locations are made publicly available through online advertising, little is known about why they choose to locate where they do. In this work, we use inferential modeling to better understand the spatial distribution of illicit massage businesses within the U.S. Based on addresses web-scraped weekly from online advertisements over 6 months, we modeled illicit massage business prevalence at the census tract and county levels. We used publicly available data to characterize census tracts and counties, finding that the state in which they are located, distance to international airports, rent and income levels, racial composition, and religious presence all had significant relationships to illicit massage business presence. Illicit massage businesses operating with unethical labor practices and/or forced sexual services are not in line with just, peaceful, and inclusive societal goals, and the burdens of this industry disproportionately fall on women. While we emphasize that not all illicit massage businesses are settings of human trafficking, better understanding this industry is a key step toward better regulating it and protecting those harmed within it

Simulating Behavioral Influences on Community Flood Risk under Future Climate Scenarios

Flood risk is a function of both climate and human behavior, including individual and societal actions. For this reason, there is a need to incorporate both human and climatic components in models of flood risk. This study simulates behavioral influences on the evolution of community flood risk under different future climate scenarios using an agent‐based model (ABM). The objective is to understand better the ways, sometimes unexpected, that human behavior, stochastic floods, and community interventions interact to influence the evolution of flood risk. One historic climate scenario and three future climate scenarios are simulated using a case study location in Fargo, North Dakota. Individual agents can mitigate flood risk via household mitigation or by moving, based on decision rules that consider risk perception and coping perception. The community can mitigate or disseminate information to reduce flood risk. Results show that agent behavior and community action have a significant impact on the evolution of flood risk under different climate scenarios. In all scenarios, individual and community action generally result in a decline in damages over time. In a lower flood risk scenario, the decline is primarily due to agent mitigation, while in a high flood risk scenario, community mitigation and agent relocation are primary drivers of the decline. Adaptive behaviors offset some of the increase in flood risk associated with climate change, and under an extreme climate scenario, our model indicates that many agents relocate.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154949/1/risa13428_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154949/2/risa13428.pd

A comparison of methods for assessing power output in non‐uniform onshore wind farms

Wind resource assessments are used to estimate a wind farm’s power production during the planning process. It is important that these estimates are accurate, as they can impact financing agreements, transmission planning, and environmental targets. Here, we analyze the challenges in wind power estimation for onshore farms. Turbine wake effects are a strong determinant of farm power production. With given input wind conditions, wake losses typically cause downstream turbines to produce significantly less power than upstream turbines. These losses have been modeled extensively and are well understood under certain conditions. Most notably, validation of different model types has favored offshore farms. Models that capture the dynamics of offshore wind conditions do not necessarily perform equally as well for onshore wind farms. We analyze the capabilities of several different methods for estimating wind farm power production in 2 onshore farms with non‐uniform layouts. We compare the Jensen model to a number of statistical models, to meteorological downscaling techniques, and to using no model at all. We show that the complexities of some onshore farms result in wind conditions that are not accurately modeled by the Jensen wake decay techniques and that statistical methods have some strong advantages in practice.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142201/1/we2143-sup-0001-supplementary.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142201/2/we2143.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142201/3/we2143_am.pd

Feasibility study of PRA for critical infrastructure risk analysis

Probabilistic Risk Analysis (PRA) has been commonly used by NASA and the nuclear power industry to assess risk since the 1970s. However, PRA is not commonly used to assess risk in networked infrastructure systems such as water, sewer and power systems. Other methods which utilise network models of infrastructure such as random and targeted attack failure analysis, N-k analysis and statistical learning theory are instead used to analyse system performance when a disruption occurs. Such methods have the advantage of being simpler to implement than PRA. This paper explores the feasibility of a full PRA of infrastructure, that is one that analyses all possible scenarios as well as the associated likelihoods and consequences. Such analysis is resource intensive and quickly becomes complex for even small systems. Comparing the previously mentioned more commonly used methods to PRA provides insight into how current practises can be improved, bringing the results closer to those that would be presented from PRA. Although a full PRA of infrastructure systems may not be feasible, PRA should not be discarded. Instead, analysis of such systems should be carried out using the framework of PRA to include vital elements such as scenario likelihood analysis which are often overlooked.publishedVersio

The Use of Simulation to Reduce the Domain of “Black Swans” with Application to Hurricane Impacts to Power Systems

Recently, the concept of black swans has gained increased attention in the fields of risk assessment and risk management. Different types of black swans have been suggested, distinguishing between unknown unknowns (nothing in the past can convincingly point to its occurrence), unknown knowns (known to some, but not to relevant analysts), or known knowns where the probability of occurrence is judged as negligible. Traditional risk assessments have been questioned, as their standard probabilistic methods may not be capable of predicting or even identifying these rare and extreme events, thus creating a source of possible black swans.In this article, we show how a simulation model can be used to identify previously unknown potentially extreme events that if not identified and treated could occur as black swans. We show that by manipulating a verified and validated model used to predict the impacts of hazards on a system of interest, we can identify hazard conditions not previously experienced that could lead to impacts much larger than any previous level of impact. This makes these potential black swan events known and allows risk managers to more fully consider them. We demonstrate this method using a model developed to evaluate the effect of hurricanes on energy systems in the United States; we identify hurricanes with potentially extreme impacts, storms well beyond what the historic record suggests is possible in terms of impacts.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138843/1/risa12742_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138843/2/risa12742-sup-0001-appendix.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138843/3/risa12742.pd

Statistical Analysis of the Effectiveness of Seawalls and Coastal Forests in Mitigating Tsunami Impacts in Iwate and Miyagi Prefectures

The Pacific coast of the Tohoku region of Japan experiences repeated tsunamis, with the most recent events having occurred in 1896, 1933, 1960, and 2011. These events have caused large loss of life and damage throughout the coastal region. There is uncertainty about the degree to which seawalls reduce deaths and building damage during tsunamis in Japan. On the one hand they provide physical protection against tsunamis as long as they are not overtopped and do not fail. On the other hand, the presence of a seawall may induce a false sense of security, encouraging additional development behind the seawall and reducing evacuation rates during an event. We analyze municipality-level and sub-municipality-level data on the impacts of the 1896, 1933, 1960, and 2011 tsunamis, finding that seawalls larger than 5 m in height generally have served a protective role in these past events, reducing both death rates and the damage rates of residential buildings. However, seawalls smaller than 5 m in height appear to have encouraged development in vulnerable areas and exacerbated damage. We also find that the extent of flooding is a critical factor in estimating both death rates and building damage rates, suggesting that additional measures, such as multiple lines of defense and elevating topography, may have significant benefits in reducing the impacts of tsunamis. Moreover, the area of coastal forests was found to be inversely related to death and destruction rates, indicating that forests either mitigated the impacts of these tsunamis, or displaced development that would otherwise have been damaged

Dependent infrastructure system modeling: A case study of the St. Kitts power and water distribution systems

Critical infrastructure systems underlie the economy, national security, and health of modern society. These infrastructures have become increasingly dependent on each other, which poses challenges when modeling these systems. Although a number of methods have been developed for this problem, few case studies that model real-world dependent infrastructures have been conducted. In this paper, we aim to provide another example of such a case study by modeling a real-world water distribution system dependent on a power system. Unlike in the limited previous case studies, our case study is in a developing nation context. This makes the availability of data about the infrastructure systems in this case study very limited, which is a common characteristic of real-world studies in many settings. Thus, a main contribution of the paper is to show how one can still develop representative, useful models for systems in the context of limited data. To demonstrate the utility of these types of models, two examples of different analyses are performed, where the results provide information about the most vulnerable parts of the infrastructures and critical linkages between the power and water distribution systems.publishedVersio