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Spatial models for flood risk assessment

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  • Marco Bee
  • Roberto Benedetti
  • Giuseppe Espa

Abstract

The problem of computing risk measures associated to flood events is extremely important not only from the point of view of civil protection systems but also because of the necessity for the municipalities of insuring against the damages. In this work we propose, in the framework of an integrated strategy, an operating solution which merges in a conditional approach the information usually available in this setup. First we use a Logistic Auto-Logistic (LAM) model for the estimation of the univariate conditional probabilities of flood events. This approach has two fundamental advantages: it allows to incorporate auxiliary information and does not require the target variables to be indepen- dent. Then we simulate the joint distribution of floodings by means of the Gibbs Sampler. Finally we propose an algorithm to increase ex post the spatial autocorrelation of the simulated events. The methodology is shown to be effective by means of an application to the estimation of the flood probability of Italian hydrographic regions.

Suggested Citation

  • Marco Bee & Roberto Benedetti & Giuseppe Espa, 2007. "Spatial models for flood risk assessment," Department of Economics Working Papers 0710, Department of Economics, University of Trento, Italia.
    • Handle: RePEc:trn:utwpde:0710
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    References listed on IDEAS

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    1. Jewson,Stephen & Brix,Anders, 2005. "Weather Derivative Valuation," Cambridge Books, Cambridge University Press, number 9780521843713, January.
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    Cited by:

    1. Solaiman Afroughi & Soghrat Faghihzadeh & Majid Jafari Khaledi & Mehdi Ghandehari Motlagh & Ebrahim Hajizadeh, 2011. "Analysis of clustered spatially correlated binary data using autologistic model and Bayesian method with an application to dental caries of 3--5-year-old children," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(12), pages 2763-2774, February.

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    More about this item

    Keywords

    Flood Risk; Conditional Approach; LAM Model; Pseudo-Maximum Likelihood Estimation; Spatial Autocorrelation; Gibbs Sampler.;
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