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Modeling risks from natural hazards with generalized additive models for location, scale and shape.

David Pitt1, Stefan Trück1, Rob van den Honert1

  • 1Macquarie University, Sydney, NSW 2109, Australia.

Journal of Environmental Management
|August 31, 2020
PubMed
Summary

This study introduces a new framework using generalized additive models for location, scale and shape (GAMLSS) to better estimate natural disaster losses. The GAMLSS approach offers superior accuracy for risk management and policy development.

Keywords:
Generalized additive models for location scale and shape (GAMLSS)Natural hazardsRegional risk factorsRisk management

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Area of Science:

  • Environmental Science
  • Risk Management
  • Statistical Modeling

Background:

  • Catastrophic natural perils like bushfires, storms, and floods cause significant losses.
  • Current models often lack the flexibility to accurately capture the frequency and severity of these losses.
  • Accurate loss estimation is crucial for effective policy development and emergency planning.

Purpose of the Study:

  • To develop and evaluate a novel framework for estimating the frequency and severity of losses from natural hazards.
  • To explore the application of generalized additive models for location, scale and shape (GAMLSS) in this context.
  • To compare the performance of GAMLSS with traditional generalized linear regression models.

Main Methods:

  • Utilized generalized additive models for location, scale and shape (GAMLSS) to model catastrophic risk.
  • Employed the generalized beta distribution of the second kind (GB2) for severity of loss modeling.
  • Incorporated regional risk factors (geographical, weather, climate variables) as covariates.
  • Conducted out-of-sample validation to assess model performance.

Main Results:

  • GAMLSS demonstrated a superior fit to empirical loss data compared to generalized linear regression models.
  • The GB2 distribution provided a good fit for the severity of losses.
  • Including covariates significantly altered predicted loss distributions.
  • Out-of-sample validation supported the correct specification of the GAMLSS models.

Conclusions:

  • The GAMLSS framework offers a more accurate approach to modeling natural hazard losses.
  • Improved loss estimation can significantly aid government policy development, risk management, and emergency services planning.
  • This methodology enhances scenario planning for natural perils.