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Intro

I am sure most of you are listening or looking at some weather forecast during the day and more often than we like to see, we read news about climate change causing new temperature records or glaciers melting at accelerating rates. Today we are not going to talk about climate change or weather forecast directly, but its underlying principle, Hydrology (which is study of water movement and distribution in a physical system). We will talk about strategies to build Hydrological models and more concretely we are looking at the intersection of Machine Learning and Hydrology. For this I am talking to Moritz Feigl. Co-founder and Chief Data Scientist at Baseflow.ai

During his PhD, Moritz investigated how Hydrology can benefit from Machine Learning, and in the interview we are going to contrast and compare two main approaches in Hydrological modeling. On one side we look at process-based models that are build on a systematic understanding of the physical world and principles and on the other side, at data-driven models; like modern Deep Learning systems that are learning a input-output relationship based on observations alone.

Moritz explains different ways how to combine those traditionally opposing approaches to get the best of both worlds, increasing the accuracy of predictions and enhancing our understanding of the underlying physical systems.

References

https://www.linkedin.com/in/moritz-feigl/

https://www.linkedin.com/company/baseflow-ai-solutions/

https://baseflow.ai/

https://abstracts.boku.ac.at/search_abstract.php?paID=3&paSID=19947&paSF=&paLIST=0&language_id=DE