AI and Natural Disaster Cost Estimation. The kind of problem that is greatly expanding.
Artificial Intelligence and Machine Learning Are Important Tools to Improve Cost Estimation for Natural Disasters in Electric Utilities by Ismael Arciniegas Rueda and Parousia Rockstroh
December 4, 2020
Natural disasters in the United States cause billions of dollars of damage to electric infrastructure every year. Hurricane Zeta, for instance, left more than 2 million people without power in the Gulf States. According to the Quadrennial Energy Review by the Department of Energy, electric system outages caused by natural disasters have an economic cost of $20-$50 billion annually.
After a disaster hits a particular area, states rely heavily on the Federal Emergency Management Agency (FEMA) to fund a significant portion of repairs. But FEMA's responsiveness to affected communities depends on its ability to estimate the costs of this work quickly and accurately.
The normal procedure to approve and allocate funding requires an extensive cost estimation process—one that is particularly difficult for electric infrastructure work that can require specialized workers and equipment. In the context of natural disaster recovery, this estimating work is rife with uncertainties (and, of late, made even more difficult because of COVID-19). For instance, in Puerto Rico after Hurricane Maria, significant electric repairs needed to be performed in hard-to-reach mountainous areas, demanding helicopters with highly trained work crews. At the same time, electric infrastructure repair is critical and urgent—other repair work can't go on without it. .... "'
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