AI could transform catastrophe modelling of secondary perils
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| Tag | 1 | 2 | Valor |
|---|---|---|---|
| LDR | 00000cab a22000004b 4500 | ||
| 001 | MAP20250016200 | ||
| 003 | MAP | ||
| 005 | 20251010095008.0 | ||
| 008 | 250716e20250916gbr|| p |0|||b|eng d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a328.1 | ||
| 100 | 1 | $0MAPA20250002951$aGeer, Joshua | |
| 245 | 1 | 0 | $aAI could transform catastrophe modelling of secondary perils$cJoshua Geer |
| 520 | $aArtificial intelligence (AI) and machine learning (ML) are poised to significantly enhance catastrophe modelling for secondary perils, such as severe convective storms, derechos, tornadoes, and wildfires, by leveraging vast amounts of high-resolution atmospheric data. Karen Clark, founder of Karen Clark & Company, emphasized during the Rendez-Vous de Septembre in Monte Carlo that AI and ML can improve forecasting accuracy for events traditionally difficult to predict, like derechos and tornado touchdowns, and can also help model wildfire behavior by analyzing wind patterns that influence fire spread | ||
| 650 | 4 | $0MAPA20080600204$aCatástrofes naturales | |
| 650 | 4 | $0MAPA20080611200$aInteligencia artificial | |
| 650 | 4 | $0MAPA20080629755$aSeguro de riesgos extraordinarios | |
| 650 | 4 | $0MAPA20170005476$aMachine learning | |
| 650 | 4 | $0MAPA20080592059$aModelos predictivos | |
| 650 | 4 | $0MAPA20080608392$aRiesgos meteorológicos | |
| 650 | 4 | $0MAPA20080551292$aIncendios | |
| 773 | 0 | $tInsurance ERM: the online resource for enterprise risk management$g16 September 2025 ; 1 p. | |
| 856 | $uhttps://www.insuranceerm.com/news-comment/ai-could-transform-catastrophe-modelling-of-secondary-perils.html |