I analyze damage from hurricane strikes on the United States since 1955. Using machine learning methods to select the most important drivers for damage, I show that large errors in a hurricane's predicted landfall location result in higher damage. This relationship holds across a wide range of...

Background: This paper proposes a framework to cope with the lack of data at the time of a disaster by employing predictive models. The framework can be used for disaster human impact assessment based on the socio-economic characteristics of the affected countries. Methods: A panel data of 4252...

Hurricanes are among the costliest natural disasters in the world, with a significant portion of their impact linked to the accuracy of their forecasts. In this paper, we estimate the economic impacts of the official hurricane forecasts in the US and develop a new approach for measuring the...