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Hurricanes are devastating natural disasters. In deciding how to respond to a hurricane, in particular whether and when to evacuate, a decision-maker must weigh often highly uncertain and contradic- tory information about the future path and intensity of the storm. To effectively plan to help people during a hurricane, it is crucial to be able to predict and understand this evacuation decision. To this end, we propose a computational model of human sequential decision-making in response to a hurricane based on a Partial Ob- servable Markov Decision Process (POMDP) that models concerns, uncertain beliefs about the hurricane, and future information. We evaluate the model in two ways. First, hurricane data from 2018 was used to evaluate the model’s predictive ability on real data. Second, a simulation study was conducted to qualitatively evaluate the sequential aspect of the model to illustrate the role that the acquisition of future, more accurate information can play on cur- rent decision-making. The evaluation with 2018 hurricane season data shows that our proposed features are significant predictors and the model can predict the data well, within and across distinct hurricane datasets. The simulation results show that, across dif- ferent setups, our model generates predictions on the sequential decisions making aspect that align with expectations qualitatively and suggests the importance of modeling information.
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A Predictive Analytics Approach for Nursing Home Hurricane Evacuation
Whether to evacuate a nursing home (NH) or shelter in place in response to the approaching hurricane is one of the most complex and difficult decisions encountered by nursing home administrators. A variety of factors may affect the evacuation decision, including storm and environmental conditions, nursing home characteristics, and the dwelling residents’ health conditions. Successful prediction of evacuation decision is essential to proactively prepare and manage resources to meet the surge in nursing home evacuation demands. In current nursing home emergency preparedness literature, there is a lack of analytical models and studies for nursing home evacuation demand prediction. In this paper, we propose a predictive analytics framework by applying machine learning techniques, integrated with domain knowledge in NH evacuation research, to extract, identify and quantify the effects of relevant factors on NH evacuation from heterogeneous data sources. In particular, storm features are extracted from Geographic Information System (GIS) data to strengthen the prediction accuracy. To further illustrate the proposed work and demonstrate its practical validity, a real-world case study is given to investigate nursing home evacuation in response to recent Hurricane Irma in Florida. The prediction performance among different predictive models are also compared comprehensively.
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- Award ID(s):
- 1825725
- PAR ID:
- 10129144
- Date Published:
- Journal Name:
- Proceedings of the 2019 IISE Annual Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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