Analyzing evacuation decisions using multi-attribute utility theory (MAUT)

Emergency managers are faced with critical evacuation decisions. These decisions must balance conflicting objectives as well as high levels of uncertainty. Multi-Attribute Utility Theory (MAUT) provides a framework through which objective trade-offs can be analyzed to make optimal evacuation decisions. This paper is the result of data gathered during the European Commission Project, Evacuation Responsiveness by Government Organizations (ERGO) and outlines a preliminary decision model for the evacuation decision. The illustrative model identifies levels of risk at which point evacuation actions should be taken by emergency managers in a storm surge scenario with forecasts at 12 and 9 hour intervals. The results illustrate how differences in forecast precision affect the optimal evacuation decision. Additional uses for this decision model are also discussed along with improvements to the model through future ERGO data-gathering

Information Retrieval and Classification of Real-Time Multi-Source Hurricane Evacuation Notices

For an approaching disaster, the tracking of time-sensitive critical information such as hurricane evacuation notices is challenging in the United States. These notices are issued and distributed rapidly by numerous local authorities that may spread across multiple states. They often undergo frequent updates and are distributed through diverse online portals lacking standard formats. In this study, we developed an approach to timely detect and track the locally issued hurricane evacuation notices. The text data were collected mainly with a spatially targeted web scraping method. They were manually labeled and then classified using natural language processing techniques with deep learning models. The classification of mandatory evacuation notices achieved a high accuracy (recall = 96%). We used Hurricane Ian (2022) to illustrate how real-time evacuation notices extracted from local government sources could be redistributed with a Web GIS system. Our method applied to future hurricanes provides live data for situation awareness to higher-level government agencies and news media. The archived data helps scholars to study government responses toward weather warnings and individual behaviors influenced by evacuation history. The framework may be applied to other types of disasters for rapid and targeted retrieval, classification, redistribution, and archiving of real-time government orders and notifications

Striving for improvement : The perceived value of improving hurricane forecast accuracy

Hurricanes are the costliest type of natural disaster in the United States. Every year, these natural phenomena destroy billions of dollars in physical capital, displace thousands, and greatly disrupt local economies. While this damage will never be eliminated, the number of fatalities and the cost of preparing and evacuating can be reduced through improved forecasts. This paper seeks to establish the public\u27s willingness to pay for further improvement of hurricane forecasts by integrating atmospheric modeling and a double-bounded dichotomous choice method in a large-scale contingent valuation experiment. Using an interactive survey, we focus on areas affected by hurricanes in 2018 to elicit residents\u27 willingness to pay for improvements along storm track, wind speed, and precipitation forecasts. Our results indicate improvements in wind speed forecast are valued the most, followed by storm track and precipitation, and that maintaining the current annual rate of error reduction for another decade is worth between $90.25 and$121.86 per person in vulnerable areas. Our study focuses on areas recently hit by hurricanes in the United States, but the implications of our results can be extended to areas vulnerable to tropical cyclones globally. In a world where the intensity of hurricanes is expected to increase and research funds are limited, these results can inform relevant agencies regarding the effectiveness of different private and public adaptive actions, as well as the value of publicly funded hurricane research programs

Finalist—2017 M&SOM Practice-Based Research Competition—The Hurricane Decision Simulator: A Tool for Marine Forces in New Orleans to Practice Operations Management in Advance of a Hurricane

The U.S. Marine Forces Reserve (MFR) in New Orleans is frequently threatened by hurricanes. To protect the safety of personnel and their families while maintaining mission capability, the Commander must make timely decisions to set up an alternate headquarters and allow for an orderly evacuation. The MFR relies on forecasts from the National Hurricane Center, but these forecasts are uncertain, are updated frequently, and can be difficult to interpret in the context of the MFR\u27s decision timeline. In addition, there are few opportunities to learn from experience.We developed the Hurricane Decision Simulator (HDS) to allow MFR personnel to practice making preparation decisions in the context of many realistic simulated storms and forecasts, and to develop a better understanding of the decision sequence, the forecast products and their relationship. The HDS has improved MFR\u27s training and readiness for hurricane preparation operations and decision making by enabling more and better focused training and is being extended to other facilities

Tropical Cyclone Intensity Estimation Using Temporal And Spatial Features From Satellite Data

Accurate intensity estimation of tropical cyclones (TC) is an important topic of research due to its economic impact and public safety concerns. An accurate measure of the current wind strength is necessary to accurately predict TC intensity. We have developed and tested automated method to estimate TC intensity based on the existing historical satellite images alone. The Hurricane Satellite data (HURSAT-B1) is used to develop the algorithm, which focuses on the North Atlantic from 1978-2009. The algorithm is trained and validated using aircraft reconnaissance-based data. Here, the data is restricted to include only fixes that are over water and are south of 45oN. This subset comprises of 2,016 measurements in 165 storms from 1988 through 2006. The proposed intensity estimation algorithm has two parts: temporal constraints and spatial (image) analysis. The temporal analysis uses the age of the cyclone, 6, 12 and 24 hour prior intensities as predictors of the expected intensity. The 10 closest analogs determined by a K-nearest-neighbor algorithm are averaged to obtain an estimate of the intensity of unknown TC. The resulting average mean absolute error is 4.8 kt (50% of estimates have MAE within 4.4 kt). The current analysis has the potential to decrease the DT noise and to provide new temporal constraints on DT. The image intensity estimation algorithm uses satellite images for intensity analysis. The expected intensity is estimated using the current image and earlier images from the 6, 12 and 24 hour before the current image as predictors. Several tests were implemented to statistically justify the proposed algorithm using the n-fold cross-validation where n is 165. The resulting average mean absolute error for the 165 storms is 10.9 kt (50% of points are within 10 kt) or 8.4 mb (50% of points are within 8 mb) and its accuracy is on par with other objective techniques. The proposed approach is an important tool for estimating the intensity of tropical cyclones due to its simplicity, objectivity and consistency

Audits and logistic regression, deciding what really matters in service processes: a case study of a government funding agency for research grants

Governmental agencies, the back office of private firms and nongovernmental organizations experience bureaucratic processes that are often repetitive and out-of-date. These imperfections cause resource misuse and support activities that diminish to the value of the process. An important element of these bureaucratic processes is checking whether certain projects approved by the office have actually been successful in their proposed objectives. Banks and credit card companies must evaluate whether creditors have fulfilled their supposed financial worthiness, tax authorities need to classify sectors of the economy and types of tax payers for probable defaults, and research grants approved by government funding agencies should verify the use of public funds by grant recipients. In this study, logistic regression is used to estimate the probability of conformity of research grants to the financial obligations of the researcher analyzing the correlation between certain characteristics of the grant and the grant´s final status as approved or not. The logistic equation uncovers those characteristics that are most important in judging status, and supports the analysis of results as false positives and false negatives. A ROC curve is constructed which reveals not only an optimal cutoff separating conformity from nonconformity, but also discloses weak links in the chain of activities that could be easily corrected and consequently public resources preserved

Exploring Uncertainties in Households’ Hurricane Evacuations

Hurricane Matthew was the most powerful hurricane during the Atlantic Hurricane Season in 2016. It caused tremendous damages to infrastructure and coastal areas of the United States. This thesis uses survey data collected in 2017 from residents in the Jacksonville Metropolitan Area after Hurricane Matthew. Survey questions were designed to capture evacuation-related decisions, information sources usage, socio-economic factors, perceived certainty and intra-familial interactions. The first part of the thesis modeled households’ perceived certainty to identify factors that affect different perceived certainty topics. Certainty topics included were: whether one lives in an evacuation zone, time of hurricane impact, evacuation preparation time needed, when to evacuate, evacuation travel mode, evacuation route, and evacuation destination. The modeling results showed similarities and disparities among perceived certainty topics. Household archetypes were created to offer insights for both decision makers and stakeholders for hurricane emergency management. The second part of this thesis explored the connection between the evacuation decision and perceived certainty using a two-stage modeling concept. Adding contextual factors usually leads to endogeneity bias which means parameters of variables will be overestimated or underestimated. A control function approach was used to account for potential endogeneity bias when linking perceived certainty with the evacuate/stay decision caused by unobserved attributes. The uncorrected base model was found to have a downward bias of the perceived certainty of evacuation destination, and with endogeneity bias corrected the parameter for this variable increased by 91.6%

EVACUATION PLANS FOR NAVAL STATION NEWPORT AND AQUIDNECK ISLAND UNDER UNCERTAINTY

Aquidneck Island, RI, is vulnerable to hurricanes; hence, evacuation plans are critical to the welfare of on-island military installations and communities. Coordination among communities is important as there are few on-island shelters and evacuation will require military and civilian evacuees to egress across the same bridges. Previous work studied optimal vehicle routing to minimize clearance times and coordinate evacuation. However, past work does not consider uncertainty involved in go (evacuate)/no-go (shelter-in-place) decisions. Under hurricane conditions, high winds will force bridge closures and calling an evacuation too late forces populations to shelter-in-place. In contrast, calling an evacuation too early for threatening, non-striking storms might direct evacuees off-island toward danger. We develop a model that can consider these tensions by combining synthetic forecasts for past storms, stochastic hurricane trajectory, expected evacuation demands, and optimal routing. We apply our model to two historical storms: Hurricane Bob in 1991 and Hurricane Gloria in 1985. Results show our model performs well for striking storms by evacuating the majority of vulnerable communities. However, our model also leads to large evacuations for threatening, non-striking storms. We conclude our model forms a good basis for evacuation planning yet needs additional analysis prior to use.Strategic Environmental Research and Development Program (SERDP), 4800 Mark Center Drive, Suite 16F16, Alexandria, VA 22350Ensign, United States NavyApproved for public release. Distribution is unlimited