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Ugur Öztürk

Bauingenieur

Name	Ugur Öztürk
Zeit	2019- (Wirkungsdaten)
Ort	Potsdam (Wirkungsort) Istanbul (Wirkungsort) Deutschland
Akademischer Grad bzw. Titel	Dr.
Beruf	Bauingenieur (charakteristischer Beruf)
Variante	U. Öztürk Ugur Ozturk
Beziehung	Universität Potsdam Deutsches GeoForschungsZentrum İstanbul Teknik Üniversitesi
Weitere Angaben	Seit 2020 Wissenschaftlicher Mitarbeiter, Universität Potsdam, Institut für Umweltwissenschaften und Geographie, Forschungsschwerpunkt Earth and Environmental Systems Seit 2018 Wissenschaftlicher Mitarbeiter am Deutschen GeoForschungsZentrum Potsdam, Section 2.6 - Seismic Hazard and Risk Dynamics Dissertation Universität Potsdam, Mathematisch-Naturwissenschaftliche Fakultät, 2019
Quelle	https://www.uni-potsdam.de/de/umwelt/institut/alle-mitarbeiterinnen/oeztuerk-ugur (Stand: 04.06.2024)
Datensatztyp	Person
GND-Nummer	1185225234
Datensatz-ID	1185225234
Beteiligt an alle anzeigen Landslide topology uncovers failure movements Landsifier v1.0: a Python library to estimate likely triggers of mapped landslides Landsifier v1.0: a Python library to estimate likely triggers of mapped landslides More than heavy rain turning into fast-flowing water – a landscape perspective on the 2021 Eifel floods Can global rainfall estimates (satellite and reanalysis) aid landslide hindcasting? How robust are landslide susceptibility estimates? Optimal design of hydrometric station networks based on complex network analysis How robust are landslide susceptibility estimates? Optimal design of hydrometric station networks based on complex network analysis A network-based comparative study of extreme tropical and frontal storm rainfall over Japan Effects of finite source rupture on landslide triggering: the 2016 Mw 7.1 Kumamoto earthquake Effects of finite source rupture on landslide triggering: The 2016 M W 7.1 Kumamoto earthquake Optimal Design of Hydrometric Station Networks Based on Complex Network Analysis Learning more to predict landslides

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Ugur Öztürk

Landslide topology uncovers failure movements

Landsifier v1.0: a Python library to estimate likely triggers of mapped landslides

Landsifier v1.0: a Python library to estimate likely triggers of mapped landslides

More than heavy rain turning into fast-flowing water – a landscape perspective on the 2021 Eifel floods

Can global rainfall estimates (satellite and reanalysis) aid landslide hindcasting?

How robust are landslide susceptibility estimates?

Optimal design of hydrometric station networks based on complex network analysis

How robust are landslide susceptibility estimates?

Optimal design of hydrometric station networks based on complex network analysis

A network-based comparative study of extreme tropical and frontal storm rainfall over Japan

Effects of finite source rupture on landslide triggering: the 2016 Mw 7.1 Kumamoto earthquake

Effects of finite source rupture on landslide triggering: The 2016 M W 7.1 Kumamoto earthquake

Optimal Design of Hydrometric Station Networks Based on Complex Network Analysis

Learning more to predict landslides