TY - GEN
T1 - Data-driven probabilistic scenarios simulation model and visualization for hurricane impact
AU - Mirchandani, Pitu
AU - Ayu, Ketut Gita
AU - Maciejewski, Ross
N1 - Funding Information:
This material is based upon work supported by the U.S. Department of Homeland Security under Grant Award Number 17STQAC00001-02-00. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security. Thanks to Brandon Matthis for developing the hurricane path visualization and Haoxiang from Northwestern University for providing the wind speed and storm surge dataset.
PY - 2019
Y1 - 2019
N2 - The 2017 Atlantic hurricane season was horrendous and became one of the costliest hurricanes in the United States. Understanding its highly erratic movement with changing speed direction and precipitation, access to storm forecast models is essential for local and state officials and emergency responders as these models provide critical storm information (e.g., possible storm trajectories and potential impacts) that acts as basis in decision making under uncertainty for preparedness and response operations. Unfortunately, access to these sophisticated models is limited or come at substantial costs. We developed a data-driven simulation model that takes possible hurricane trajectories, referred here as scenarios, as inputs, coupled with publicly available rolling horizon forecasts of overall weather while incorporating hurricane characteristics (wind speeds, precipitation and storm surges) to generate spatial-temporal storm predicted impacts at scenario level. The model also estimates the scenario probabilities, not directly provided in the public forecasts. The computational results on hurricane Irma case study illustrates the scenario-level storm impacts are generated with fairly accurately within reasonably short runtimes. In the next phase of research not reported here, these predictions impacts can drive models for disruptions in transportation, supply chains and power grids
AB - The 2017 Atlantic hurricane season was horrendous and became one of the costliest hurricanes in the United States. Understanding its highly erratic movement with changing speed direction and precipitation, access to storm forecast models is essential for local and state officials and emergency responders as these models provide critical storm information (e.g., possible storm trajectories and potential impacts) that acts as basis in decision making under uncertainty for preparedness and response operations. Unfortunately, access to these sophisticated models is limited or come at substantial costs. We developed a data-driven simulation model that takes possible hurricane trajectories, referred here as scenarios, as inputs, coupled with publicly available rolling horizon forecasts of overall weather while incorporating hurricane characteristics (wind speeds, precipitation and storm surges) to generate spatial-temporal storm predicted impacts at scenario level. The model also estimates the scenario probabilities, not directly provided in the public forecasts. The computational results on hurricane Irma case study illustrates the scenario-level storm impacts are generated with fairly accurately within reasonably short runtimes. In the next phase of research not reported here, these predictions impacts can drive models for disruptions in transportation, supply chains and power grids
KW - Emergency response
KW - Hurricane impacts
KW - Weather simulation
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M3 - Conference contribution
AN - SCOPUS:85095438522
T3 - IISE Annual Conference and Expo 2019
BT - IISE Annual Conference and Expo 2019
PB - Institute of Industrial and Systems Engineers, IISE
T2 - 2019 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2019
Y2 - 18 May 2019 through 21 May 2019
ER -