Samuel Roeslin

Ispra, Italy

sroeslin@hotmail.com

samuel.roeslin@ec.europa.eu

Hello, I’m Sam Roeslin 👋

I am working as a scientific project officer at the Joint Research Centre (JRC) of the European Commission in Ispra, Italy.

My research interests are in the application of data science and novel technologies for natural disaster risk analysis, mitigation and management.

I received my PhD in civil engineering from the University of Auckland, New Zealand, master (M. Eng.) from the Regensburg University of Applied Sciences , Germany, and bachelors (B. Eng., B. Sc., Licence Professionnelle), from the Karlsruhe University of Applied Sciences, Germany, University of Applied Sciences Northwestern Switzerland, Switzerland, and Robert Schuman University Institute of Technology, France.

When not behind my computer, you can find me on my mountain bike 🚴 or hiking enjoying the outdoors ⛰️

news

Jul 3, 2023	I have joined the unit E.1 Disaster Risk Management as Scientific Project Officer – Geospatial data analysis for the Risk Data Hub. I’ll be working on further developing the DRMKC Risk Data Hub.
May 16, 2023	I am happy to let you know that I started a trainee at the Joint Research Centre (JRC) of the European Commission in Ispra, Italy. I am working in the unit C.2 Energy Efficiency and Renewables in the European Solar Test Installation (ESTI) lab on the project: “Data analysis of the electrical output performance of bifacial photovoltaics (PV) devices under long-term outdoor conditions”.
Apr 26, 2023	We presented our work on the development of a seismic loss prediction model for residential buildings using machine learning (ML) in Christchurch, New Zealand at the online poster session of the EGU23. The programme of the session is available at Session ITS1.1/NH0.1 - Artificial Intelligence for Natural Hazard and Disaster Management.
Mar 22, 2023	Our article on the development of a seismic loss prediction model for residential buildings using machine learning (ML) in Christchurch, New Zealand has been published. It is available in the NHESS journal https://doi.org/10.5194/nhess-23-1207-2023.
Sep 28, 2022	🎓 Spring Graduation 2022 🎓 Thanks to everyone who helped and supported me during the PhD time. September 2022 Graduations - Ceremony 3.

selected publications

NHESS

Development of a seismic loss prediction model for residential buildings using machine learning – Ōtautahi, Christchurch, New Zealand

Roeslin, S., Ma, Q., Chigullapally, P., Wicker, J., and Wotherspoon, L.

Natural Hazards and Earth System Sciences 2023

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The September 19th, 2017 Puebla, Mexico earthquake

Roeslin, Samuel, Juárez-Garcia, Hugón, Elwood, Kenneth, Dhakal, Rajesh, and Gómez-Bernal, Alonso

Bulletin of the New Zealand Society for Earthquake Engineering Sep 2020

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Eq Spectra

A machine learning damage prediction model for the 2017 Puebla-Morelos, Mexico, earthquake

Roeslin, Samuel, Ma, Quincy, Juárez-Garcia, Hugon, Gómez-Bernal, Alonso, Wicker, Joerg, and Wotherspoon, Liam

Earthquake Spectra Jul 2020

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The 2017 Puebla, Mexico, earthquake event led to significant damage in many build-ings in Mexico City. In the months following the earthquake, civil engineering stu-dents conducted detailed building assessments throughout the city. They collectedbuilding damage information and structural characteristics for 340 buildings in theMexico City urban area, with an emphasis on the Roma and Condesa neighborhoodswhere they assessed 237 buildings. These neighborhoods are of particular interestdue to the availability of seismic records captured by nearby recording stations, andpreexisting information from when the neighborhoods were affected by the 1985Michoaca ́n earthquake. This article presents a case study on developing a damageprediction model using machine learning. It details a framework suitable for workingwith future post-earthquake observation data. Four algorithms able to perform clas-sification tasks were trialed. Random forest, the best performing algorithm, achievesmore than 65% prediction accuracy. The study of the feature importance for the random forest shows that the building location, seismic demand, and building height arethe parameters that influence the model output the most.