Dr. Ralf Loritz
KIT-Junior Research Leader & KIT Associate Fellow, Institute for Water and Environment – Hydrology (IWU/HYD), KIT
The lecture will demonstrate how machine learning (ML) is transforming hydrology – and why these technologies have become a key tool for predicting floods, droughts, and other hydrological extremes.
Hydrology deals with the water cycle in all its facets
– from precipitation to storage in the soil to runoff into rivers
and lakes. Mathematical and physical models have been used for decades
to understand these processes and predict future events. They represent
the well-known laws of hydrology – such as infiltration,
evaporation, and runoff formation – through equations, thereby
enabling a quantitative description of water movement across the
landscape.
But are new, data-driven approaches superior to
these models? What are the respective advantages and disadvantages of
each method? These questions are at the heart of the
lecture.
Using examples from current research projects, such as the BMFTR-funded collaborative project KI-HopE, the talk will also demonstrate how current concepts are being implemented in practice – for instance, in the development of a Germany-wide, machine learning-based flood forecasting system in cooperation with the Deutscher Wetterdienst and the flood forecasting centers.
This presentation invites us to rethink hydrology as a data science discipline – how can an understanding of physics and artificial intelligence help us better identify the risks of flooding and drought and address them earlier?
The presentation will also introduce the KIT Graduate School of Computational and Data Science.
Angela Hühnerfuß, M.A.
Managing Director, KIT Graduate School Computational and Data Science, KIT
The KIT Graduate School Computational and Data Science | KCDS at KIT Center MathSEE is an English-language graduate school for all doctoral researchers at KIT who are working on interdisciplinary, data- and model-driven research projects.
What sets KCDS apart is the tandem principle: Each doctoral candidate has one advisor from the mathematical sciences with a focus on methodology and one from the SEE discipline (natural sciences, economics, and engineering) with a focus on application. The interdisciplinary training program at KCDS offers doctoral candidates the opportunity to pursue targeted professional and interdisciplinary development, expand their network, and organize their own projects and events.
---------------------
Anmeldung für Einzeltermine
Studierende, Promovierende, Mitarbeitende und registrierte Gasthörende am KIT können vorbehaltlich freier Plätze an Einzelterminen der Ringvorlesung teilnehmen.
Melden Sie sich in diesem Fall bitte per E-Mail an dataliteracy∂forum kit edu für die entsprechenden Termine an.
Bitte beachten Sie die Voraussetzungen zur Teilnahme unter https://www.forum.kit.edu/d ali.php#rv.
---------------------
Registration for individual sessions
Students, doctoral candidates, KIT employees, and registered guest students at KIT may attend individual sessions of the lecture series, subject to availability.
In this case, please register for the relevant sessions by emailing dataliteracy∂forum kit edu.
Please review the participation requirements at https://www.forum.kit.edu/d ali.php#rv.
Dr. Ralf Loritz
\n\nKIT-Junior Research Leader & KIT Associate Fellow, Institute for Water and Environment – Hydrology (IWU/HYD), KIT
\n\n\n\n
The lecture will demonstrate how machine learning (ML) is transforming hydrology – and why these technologies have become a key tool for predicting floods, droughts, and other hydrological extremes.
\n\n\n\n
Hydrology deals with the
water cycle in all its facets – from precipitation to storage in the
soil to runoff into rivers and lakes. Mathematical and physical models
have been used for decades to understand these processes and predict
future events. They represent the well-known laws of hydrology – such
as infiltration, evaporation, and runoff formation – through
equations, thereby enabling a quantitative description of water movement
across the landscape.
\nBut are new, data-driven approaches
superior to these models? What are the respective advantages and
disadvantages of each method? These questions are at the heart of the
lecture.
\n\n
Using examples from current research projects, such as the BMFTR-funded collaborative project KI-HopE, the talk will also demonstrate how current concepts are being implemented in practice – for instance, in the development of a Germany-wide, machine learning-based flood forecasting system in cooperation with the Deutscher Wetterdienst and the flood forecasting centers.
\n\n\n\n
This presentation invites us to rethink hydrology as a data science discipline – how can an understanding of physics and artificial intelligence help us better identify the risks of flooding and drought and address them earlier?
\n \n\n\n\n
The presentation will also introduce the KIT Graduate School of Computational and Data Science.
\nAngela Hühnerfuß, M.A.
\n\nManaging Director, KIT Graduate School Computational and Data Science, KIT
\n\n\n\n
The KIT Graduate School Computational and Data Science | KCDS at KIT Center MathSEE is an English-language graduate school for all doctoral researchers at KIT who are working on interdisciplinary, data- and model-driven research projects.
\n\nWhat sets KCDS apart is the tandem principle: Each doctoral candidate has one advisor from the mathematical sciences with a focus on methodology and one from the SEE discipline (natural sciences, economics, and engineering) with a focus on application. The interdisciplinary training program at KCDS offers doctoral candidates the opportunity to pursue targeted professional and interdisciplinary development, expand their network, and organize their own projects and events.
\n\n\n\n
---------------------
\n\nAnmeldung für Einzeltermine
\n\nStudierende, Promovierende, Mitarbeitende und registrierte Gasthörende am KIT können vorbehaltlich freier Plätze an Einzelterminen der Ringvorlesung teilnehmen.
\n\nMelden Sie sich in diesem Fall bitte per E-Mail an dataliteracy∂forum kit edu für die entsprechenden Termine an.
\n\n\n\n
Bitte beachten Sie die Voraussetzungen zur Teilnahme unter https://www.forum.kit.edu/d ali.php#rv.
\n\n\n\n
---------------------
\n\nRegistration for individual sessions
\n\nStudents, doctoral candidates, KIT employees, and registered guest students at KIT may attend individual sessions of the lecture series, subject to availability.
\n\nIn this case, please register for the relevant sessions by emailing dataliteracy∂forum kit edu.
\n\n\n\n
Please review the participation requirements at https://www.forum.kit.edu/d ali.php#rv.
\n\n\n
\n DTSTART;TZID=Europe/Berlin:20270127T154500 DTEND;TZID=Europe/Berlin:20270127T171500 LOCATION:Geb. 50.28 InformatiKOM II, R 010 (SR 2) END:VEVENT END:VCALENDAR