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

Using historical utility outage data to compute overall transmission grid resilience

Given increasing risk from climate-induced natural hazards, there is growing interest in the development of methods that can quantitatively measure resilience in power systems. This work quantifies resilience in electric power transmission networks in a new and comprehensive way that can represent the multiple processes of resilience. A novel aspect of this approach is the use of empirical data to develop the probability distributions that drive the model. This paper demonstrates the approach by measuring the impact of one potential improvement to a power system. Specifically, we measure the impact of additional distributed generation on power system resilience

Type II diabetes emergency room visits associated with Hurricane Sandy in New Jersey: implications for preparedness

On October 29, 2012, Hurricane Sandy made landfall in New Jersey, causing major power outages, flooded roads, and disruption of public transportation. Individuals diagnosed with diabetes may be especially vulnerable to natural disasters because of limited access to medications or use of glucose monitoring devices. We examined changes in emergency room visits (ERVs) for type II diabetes mellitus potentially associated with Hurricane Sandy in New Jersey. Data analyzed in 2014 included ERVs to general acute care hospitals in New Jersey among residents of three counties with a primary or secondary type II diabetes diagnosis (PDD or SDD) in 2011–2012. Compared to the previous year, results showed an 84% increased rate of PDD ERVs during the week of Hurricane Sandy, after adjusting for age and sex (rate ratio (RR) = 1.84, 95% confidence interval (CI) 1.12, 3.04). Results were nonsignificant for SDD (RR = 0.94, 95% CI 0.83, 1.08). Spatial analysis showed the increase in visits was not consistently associated with flood zone areas. We observed substantial increases in ERVs for primary type II diabetes diagnoses associated with Hurricane Sandy in New Jersey. Future public health preparedness efforts during storms should include planning for the healthcare needs of populations living with diabetes

Improving Hurricane Power Outage Prediction Models Through the Inclusion of Local Environmental Factors

Tropical cyclones can significantly damage the electrical power system, so an accurate spatiotemporal forecast of outages prior to landfall can help utilities to optimize the power restoration process. The purpose of this article is to enhance the predictive accuracy of the Spatially Generalized Hurricane Outage Prediction Model (SGHOPM) developed by Guikema et al. (2014). In this version of the SGHOPM, we introduce a new two‐step prediction procedure and increase the number of predictor variables. The first model step predicts whether or not outages will occur in each location and the second step predicts the number of outages. The SGHOPM environmental variables of Guikema et al. (2014) were limited to the wind characteristics (speed and duration of strong winds) of the tropical cyclones. This version of the model adds elevation, land cover, soil, precipitation, and vegetation characteristics in each location. Our results demonstrate that the use of a new two‐step outage prediction model and the inclusion of these additional environmental variables increase the overall accuracy of the SGHOPM by approximately 17%.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147200/1/risa12728_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147200/2/risa12728.pd

Simulating Long-Term and Short-Term Community and Infrastructure Vulnerability and Response to Natural Hazards

Natural disasters can cause severe damage to infrastructure, the economy, and human lives. A better understanding of the vulnerability of individual households, critical infrastructure, and a community can be vital to in both short-term disaster response and long-term resilience planning. This dissertation develops methods to analyze the long-term vulnerability of communities when facing repeated hurricanes and heat waves in a changing climate, as well as the short-term vulnerability of power systems under different types of disasters. The approaches I develop are a combination of simulation modeling, predictive modeling, and network theory. The first chapter of this dissertation describes the vulnerability of communities when facing different hazardous events, such as hurricanes, heat waves, and power outages. The second chapter focuses on enhancing understanding of the long-term vulnerability of a community under repeated hurricanes. I discuss how learning, initial beliefs and memory decay effects influence individual decisions and change regional vulnerability under different hurricane climate scenarios. We found how different initial knowledge and the memory effect can result in different community vulnerability under different hurricane climate scenarios. The third chapter develops methods for estimating power system damage and power outages from extreme weather events. I use publicly available data to generate the layout of the distribution system which is not publicly available in most cases. I then use the synthetic distribution layout to simulate damage and power outages from weather events. This model can provide important information to understand regional and individual power outage risks. The fourth chapter studies how the long-term vulnerability of a community may change under repeated heat waves. I build an agent-based model to address the interplay of the vulnerability of community, climate change, individual mitigation, social networks, power outages, and government mitigation. The model shows how each component is triggering the evolution of community heat vulnerability with historical events and what-if scenarios. The fifth chapter summarizes this dissertation and discusses potential limitations and future directions of this study. Overall, this work develops new methods to study the vulnerability of communities under repeat natural disasters aiming to enhance better short-term response and long-term planning. These models can help decision-makers, policy-makers, and individuals to make better plans when facing unexpected extreme weather events.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167902/1/cwzhai_1.pd

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

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

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

Asset Risk Management of Electric Power Grids

ABSTRACT Asset Risk Management for Electric Power Grids Niloufar Youssefi Civil Infrastructure is essential for the quality of life in developed and developing countries. Since electric power supply is needed for the operation of other vital infrastructure, it is ranked as the highest critical infrastructure. There are substantial adverse impacts on society when power grids fail, resulting in interruption and/or degradation of services. Such failure can cause heavy traffic congestions resulting from nonfunctioning traffic lights, and disturbances for other critical infrastructure elements such as water and sewage treatment plants. In order to ensure reliability of the bulk power system (BPS) in North America, the North American Electric Reliability Corporation (NERC) requires that power companies submit reports when sufficiently enormous instabilities happen within their territories in order to share the experiences and lessons learned, and to suggest solutions that utilities can apply to their procedures during unusual situations. To simplify and organize information, the NERC has divided the BPS of North America into eight zones, three of which consist of both US states and Canadian provinces. The research presented here focuses on the Canadian part of NPCC zone which covers Quebec, Ontario, New Brunswick and Nova Scotia. The main purpose of this research is to identify factors affecting power outages in the eastern Canada and develop a model for predicting the likelihood of power outage occurrences based on weather forecasted data. For this reason, System Disturbances Reports from 1992 to 2009 have been scrutinized to determine the conditions in which an attack on power grids can likely happen. According to these reports, various reasons were found to trigger power outages, including equipment failure, voltage reduction, human error, etc. However, weather conditions are the paramount cause of unavailability of power service in the northeastern district. Weather conditions variables such as wind speed, temperature, humidity, precipitation and lightning are obtained for those same periods from the Environment Canada database. In addition, in two other variables (i.e. electric consumption index and electric network size) are considered as the factors that are likely to impact power outage incidents indirectly. Based on historical data gathered for weather conditions and power outages, different types of Artificial Neural Network models (i.e. BPNN, GRNN, and PNN) were studied and developed to predict the likely occurrence of power outage utilizing weather forecasted data for four eastern Canadian provinces. Two types of datasets are used for training the models: Dataset I considers the extreme values for all the weather variables, and Dataset II, which consists the extreme value for wind speed (the most critical factor affecting the power grids) plus the values of the other weather variables at the same time that the wind speed reached its maximum value. The results indicate that the best performing model is PNN that was trained with Dataset I for it provides more accurate results. The model is also trained using Quebec dataset, which indicates that data for a specific location is expected to lead to better results. Social cost for electric power outage are then estimated four sectors; residential, commercial, industrial and agriculture. As a result, once the average duration of power outage is recognized as well as its likelihood of occurrence, the social cost of that power failure could be estimated in the four sectors. The present research helps power companies to predict the likelihood of electric power outage based on weather forecasting data. Furthermore, they are able to estimate the social cost of electric power failure in advance. This will provide useful information for further actions in risk mitigation, and will aide professionalisms in the process of creating choices to improve opportunities and to lessen threats