Enhancing Wildfire and Flood Risk Assessment with random forest. Insights into Machine Learning in Munich Re’s Natural Catastrophe Model Development

AGIT Conference. Kienmoser, Dominik: Enhancing Wildfire and Flood Risk Assessment with random forest. Insights into Machine Learning in Munich Re’s Natural Catastrophe Model Development. Salzburg : Universitätsbibliothek Salzburg, 2025

Publication Details

Journal: ePLUS
Published: 2025-01-01
DOI: https://doi.org/10.25598/agit/2025-41
Primary Topic: Forest Ecology and Biodiversity Studies
Type: article
Field-Weighted Citation Impact: 0.00

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article

Enhancing Wildfire and Flood Risk Assessment with random forest. Insights into Machine Learning in Munich Re’s Natural Catastrophe Model Development

Jan 1, 2025

ePLUS

Forest Ecology and Biodiversity Studies

article

Enhancing Wildfire and Flood Risk Assessment with random forest. Insights into Machine Learning in Munich Re’s Natural Catastrophe Model Development

January 1, 2025article en

Abstract

ePLUS

Climate action

Openalex Percentile: Top 53%

Forest Ecology and Biodiversity Studies

DOI Open Access PDF OpenAlex

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Enhancing Wildfire and Flood Risk Assessment with random forest. Insights into Machine Learning in Munich Re’s Natural Catastrophe Model Development · ePLUS (2025) | TGRS Research Map | TGRS