Vojtek et al. (2026) Data for: Rapid and high-resolution prediction of fluvial flood inundation using machine learning models trained on hydraulically derived data and river segmentation

Identification

• Journal: Mendeley Data
• Year: 2026
• Date: 2026-01-08
• Authors: Matej Vojtek, Dávid Držík, Jozef Kapusta, Jana Vojteková
• DOI: 10.17632/t3rfrp7fsw

Research Groups

Matej Vojtek, Dávid Držík, Jozef Kapusta, Jana Vojteková

Short Summary

This dataset provides the input and resulting data for research focused on rapid and high-resolution prediction of fluvial flood inundation, utilizing machine learning models trained on hydraulically derived data and river segmentation.

Objective

• To provide the input and resulting data necessary for developing and applying machine learning models for rapid and high-resolution prediction of fluvial flood inundation.

Study Configuration

• Spatial Scale: High-resolution (specifics not detailed in the provided text).
• Temporal Scale: Rapid (specifics not detailed in the provided text).

Methodology and Data

• Models used: Machine learning models (specific types not detailed in the provided text).
• Data sources: Hydraulically derived data and river segmentation data, used for training the machine learning models. The dataset itself comprises "Input and resulting data."

Main Results

• The provision of a comprehensive dataset comprising input and resulting data for fluvial flood inundation prediction using machine learning.

Contributions

• This dataset contributes to the reproducibility and further development of rapid, high-resolution fluvial flood inundation prediction methods by making the underlying data publicly available.

Funding

• Not specified in the provided text.

Citation

@article{Vojtek2026Data,
  author = {Vojtek, Matej and Držík, Dávid and Kapusta, Jozef and Vojteková, Jana},
  title = {Data for: Rapid and high-resolution prediction of fluvial flood inundation using machine learning models trained on hydraulically derived data and river segmentation},
  journal = {Mendeley Data},
  year = {2026},
  doi = {10.17632/t3rfrp7fsw},
  url = {https://doi.org/10.17632/t3rfrp7fsw}
}