Institute of Geography
Topic
Flood hazard mapping using physically-informed machine and deep learning
PhD. program
Year of admission
2026
Name of the supervisor
Doc. RNDr. Matej Vojtek, PhD.
Contact:
Receiving school
Prírodovedecká fakulta UK
Annotation
Flood hazard mapping primarily focuses on spatial representation of flood extent and flow depths. Although conventional physically-based models are highly accurate, their deployment for real-time flood mapping is limited due to high demands on data preparation and long computational times. Using the physically-informed data-driven, particularly machine and deep learning, approaches provides opportunities for fast inundation predictions as well as transferring the results to other unmapped areas. The aim of this dissertation thesis is to develop physically-informed machine and deep learning models to simulate the flood extent and flow depths based on high-resolution spatial data and data generated via the hydrologic-hydraulic models. The thesis aims to address the fluvial as well as pluvial flood types. The integration of rainfall-runoff, hydraulic, machine/deep learning, and geo-information technologies is anticipated in this dissertation thesis.