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Applied Sciences
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Flood Risk and Geo-Hazards: The Strategy for Prevention and Mitigation
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Flood Risk and Geo-Hazards: The Strategy for Prevention and Mitigation

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: 20 May 2026 | Viewed by 905

Special Issue Editor

Dr. Amithirigala Widhanelage Jayawardena

E-Mail Website
Guest Editor
Department of Civil Engineering, The University of Hong Kong, Hong Kong 999077, China
Interests: water and life; anthropogenic changes in the hydrological cycle; trends in hydrological variables; hydrological modeling; drinking water, health, food and energy nexus; water-related disasters; water-related conflicts
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Flood risks and geo-hazards are global phenomena that lead to disasters when the coping capacity in the affected region is inadequate. They include land and mudslides, volcanic eruptions, storm surges, tsunamis, tidal waves, debris flow, avalanches, droughts, and all types of cyclones. Recent disasters of this category include mudslides in India and Nepal; floods and storms in the Middle East, Brazil, the US, Europe, and China; typhoons in East Asia and the Caribbean; and tsunamis in Japan, all of which have resulted in significant casualties and billions of dollars of economic damage. It is also believed that anthropogenic factors have contributed to an increasing trend in the frequency and severity of such disasters. Since natural hazards are not preventable, the only way to minimize the risks is by providing structural and/or non-structural means of disaster mitigation. The latter are more cost-effective, and efforts need to be made to promote the development and implementation of early warning systems. This Special Issue of Applied Sciences aims to promote the development and dissemination of research efforts and results to a wider audience.

Dr. Amithirigala Widhanelage Jayawardena
Guest Editor

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Keywords

  • floods
  • land and mudslides
  • debris flow
  • earthquakes
  • tsunamis
  • avalanches
  • volcanic eruptions
  • cyclones
  • monsoons
  • international initiatives

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Published Papers (1 paper)

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Research

24 pages, 109933 KB  
Article
Deep Learning-Based Short-Term Stream-Stage and Urban Inundation Prediction in a Highly Urbanized Basin: A Case Study of Bisan-dong, Anyang, South Korea
by Youngkyu Jin, Taekmun Jeong, Yonghyeon Gwon, Jongpyo Park, Hyungjin Shin, Heesung Lim and Sang I. Park
Appl. Sci. 2026, 16(4), 1792; https://doi.org/10.3390/app16041792 - 11 Feb 2026
Viewed by 473
Abstract
Urban pluvial flooding in highly developed basins is challenging to forecast in real time because detailed 1D–2D hydraulic models are computationally expensive, while purely data-driven approaches often lack physical consistency. This study aims to enable operational urban flood nowcasting by proposing a model-informed [...] Read more.
Urban pluvial flooding in highly developed basins is challenging to forecast in real time because detailed 1D–2D hydraulic models are computationally expensive, while purely data-driven approaches often lack physical consistency. This study aims to enable operational urban flood nowcasting by proposing a model-informed AI framework for short-term stream-stage and urban inundation prediction in the Bisan-dong district of Anyang, South Korea, where the Anyang and Hagui Streams frequently overflow. A gated recurrent unit (GRU) network was trained on 10 min rainfall and stream-stage observations from 2011 to 2018 and independently validated on 2019–2022 data at four gauges to forecast stream stage at lead times of 10–60 min. In parallel, an ANN–CNN inundation surrogate was trained on 864 XP-SWMM 1D–2D simulation scenarios, forced by design storms and downstream water-level boundary conditions, to produce 256 × 256 maps of maximum inundation depth. The GRU model achieved R2 and Nash–Sutcliffe efficiency values generally above 0.95, with a mean absolute percentage error (MAPE) below approximately 5% for 10–30-min lead times; performance decreased but remained useful at 60 min. The inundation surrogate reproduced XP-SWMM results with an MAPE of 8.89% for inundation area and 19.49% for grid-based depth. Together, the ANN–CNN system enables rapid generation of high-resolution flood maps and provides a practical basis for AI-assisted urban flood nowcasting and risk management. Full article
(This article belongs to the Special Issue Flood Risk and Geo-Hazards: The Strategy for Prevention and Mitigation)
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