---
res:
  bibo_abstract:
  - Accurate flood simulation remains a significant challenge in many flood-prone
    regions, particularly in developing countries and urban areas, where the availability
    of high-resolution topographic data is especially limited. While publicly available
    digital elevation model (DEM) datasets are increasingly accessible, their spatial
    resolution is often insufficient for reflecting fine-scaled elevation details,
    which hinders the ability to simulate pluvial floods in built environments. To
    address this issue, we implemented a deep-learning-based method, which efficiently
    enhances the spatial resolution of DEM data, and quantified the effect of the
    improved DEM on flood simulation. The method employs a tailored multi-source input
    module, enabling it to effectively integrate and learn from diverse data sources.
    By utilising publicly accessible global datasets, such as low-resolution DEM datasets
    (i.e. 30 m Shuttle Radar Topography Mission, SRTM) in conjunction with high-resolution
    multispectral imagery (e.g. Sentinel-2A), our approach allows us to produce a
    super-resolution DEM, which exhibits superior performance compared to conventional
    methods in reconstructing 10 m DEM data based on 30 m DEM data and 10 m multispectral
    satellite images. We evaluated the performance of the super-resolution DEM in
    flood simulations. Compared to conventional methods (e.g. bicubic interpolation),
    the simulation results demonstrated that our approach significantly improved the
    accuracy of flood simulations, with a reduction in the mean absolute error of
    floodwater depth of about 13.1 % and an increase in the intersection over union
    (IoU) for inundation area predictions of about 46 %. Accordingly, this study underscores
    the practical value of machine learning techniques that leverage publicly available
    global datasets to generate DEMs that allow for the enhancement of flood simulations.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Yue
      foaf_name: Zhu, Yue
      foaf_surname: Zhu
  - foaf_Person:
      foaf_givenName: Paolo
      foaf_name: Burlando, Paolo
      foaf_surname: Burlando
  - foaf_Person:
      foaf_givenName: Puay Yok
      foaf_name: Tan, Puay Yok
      foaf_surname: Tan
  - foaf_Person:
      foaf_givenName: Christian
      foaf_name: Geiß, Christian
      foaf_surname: Geiß
  - foaf_Person:
      foaf_givenName: Simone
      foaf_name: Fatichi, Simone
      foaf_surname: Fatichi
      foaf_workInfoHomepage: http://www.librecat.org/personId=cf8e546b-a9b0-11f0-a43b-aa89ed1b56d6
  bibo_doi: 10.5194/nhess-25-2271-2025
  bibo_issue: '7'
  bibo_volume: 25
  dct_date: 2025^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/1561-8633
  - http://id.crossref.org/issn/1684-9981
  dct_language: eng
  dct_publisher: Copernicus Publications@
  dct_title: Improving pluvial flood simulations with a multi-source digital elevation
    model super-resolution method@
...