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catastrophe-modelling

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Location-level Florida hurricane catastrophe model in Python: HURDAT2-calibrated hazard + physical wind field (Holland, Vickery-Wadhera, Kaplan-DeMaria), synthetic exposure, HAZUS-anchored vulnerability, OED/YLT/ELT formats, XoL reinsurance with reinstatements, gross/net AEP/OEP curves & PMLs, historical backtesting (Andrew, Ian).

  • Updated Jun 14, 2026
  • Python

Pricing and trigger-design engine for rainfall-index parametric crop insurance. Gamma/Weibull index fitting, Monte Carlo pricing with risk loadings, 40-year backtest, and quantified basis-risk analysis showing 33% of genuine loss years go uncompensated.

  • Updated Jul 19, 2026
  • Python

Compatibility-first R pipeline for cross-country earthquake damage modelling. Harmonises asset-level building-damage data from Nepal (2015 Gorkha), Türkiye (2023) and Japan (2024 Noto), benchmarks within-country models, runs Nepal–Türkiye transfer, and isolates compatibility vs. classifier effects via simulation.

  • Updated May 7, 2026
  • TeX

Final Project for Leiden University Media Technology MSc. involving an advanced processing sketch that simulates the effect of catastrophe types on Faraday Wave pattern morphology during audiovisual playback. All code by Trent Eriksen and inspired by research from Amber van der Tuin

  • Updated Feb 26, 2024
  • Processing

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