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Predicting drought and subsidence risks in France
by
Charpentier, Arthur
, Ali, Hani
, James, Molly
in
Buildings
/ Catastrophes
/ Climate change
/ Costs
/ Disaster insurance
/ Drought
/ Economic impact
/ Economics
/ Environmental Sciences
/ Insurance
/ Insurance coverage
/ Insurance industry
/ Insurance policies
/ Mathematical models
/ Precipitation
/ Rain
/ Regression analysis
/ Statistical analysis
/ Statistical models
/ Subsidence
/ Trends
/ Weather forecasting
2022
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Predicting drought and subsidence risks in France
by
Charpentier, Arthur
, Ali, Hani
, James, Molly
in
Buildings
/ Catastrophes
/ Climate change
/ Costs
/ Disaster insurance
/ Drought
/ Economic impact
/ Economics
/ Environmental Sciences
/ Insurance
/ Insurance coverage
/ Insurance industry
/ Insurance policies
/ Mathematical models
/ Precipitation
/ Rain
/ Regression analysis
/ Statistical analysis
/ Statistical models
/ Subsidence
/ Trends
/ Weather forecasting
2022
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Do you wish to request the book?
Predicting drought and subsidence risks in France
by
Charpentier, Arthur
, Ali, Hani
, James, Molly
in
Buildings
/ Catastrophes
/ Climate change
/ Costs
/ Disaster insurance
/ Drought
/ Economic impact
/ Economics
/ Environmental Sciences
/ Insurance
/ Insurance coverage
/ Insurance industry
/ Insurance policies
/ Mathematical models
/ Precipitation
/ Rain
/ Regression analysis
/ Statistical analysis
/ Statistical models
/ Subsidence
/ Trends
/ Weather forecasting
2022
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Journal Article
Predicting drought and subsidence risks in France
2022
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Overview
The economic consequences of drought episodes are increasingly important although they are often difficult to apprehend, in part because of the complexity of the underlying mechanisms. In this article we will study one of the consequences of drought, namely the risk of subsidence (or more specifically clay-shrinkage-induced subsidence), for which insurance has been mandatory in France for several decades. Using data obtained from several insurers, representing about a quarter of the household insurance market over the past 20 years, we propose some statistical models to predict not only the frequency but also the intensity of these droughts for insurers. But even if we use more advanced models than standard regression-type models (here random forests to capture non-linearity and cross effects) and all geophysical and climatic information is available, it is still difficult to predict the economic cost of subsidence claims.
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