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Utilizing Geographic Information Systems for Sustainable Prediction and Prevention of Disaster Risk

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 862

Special Issue Editors


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Guest Editor
Geodesy Laboratory, Civil & Architectural and Environmental System Engineering, Sungkyunkwan University (SKKU), Suwon 16419, Gyeonggi, Republic of Korea
Interests: geodetic science; GIS; digital twin; risk assessment using geospatial information and disaster management

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Guest Editor
Geo Informatics Laboratory, School of Civil & Architecture Engineering, Sungkyunkwan University, Natural Science Campus, Suwon 16419, Republic of Korea
Interests: remote sensing; plate motion; building shape

Special Issue Information

Dear Colleagues,

Rapid climate change, urban expansion, and increasing exposure to natural hazards have intensified the global demand for advanced methodologies capable of predicting and mitigating disaster risk. Geographic Information Systems (GIS), coupled with geodetic techniques, remote sensing, and digital twin technologies, play an essential role in understanding the spatial and temporal behavior of hazards. These integrated geospatial approaches provide a scientific foundation for early warning, vulnerability assessment, and sustainable disaster management.

This Special Issue aims to present innovative GIS-based methods, analytical frameworks, and data-driven solutions that enhance sustainable disaster prediction and prevention. This focus aligns closely with the journal’s scope by advancing geospatial science, environmental assessment, and the application of emerging technologies to improve societal resilience and support informed decision-making.

In this Special Issue, original research articles and reviews are welcome. Research areas may include, but are not limited to, the following: GIS-based hazard prediction and mapping; geospatial digital twins for real-time disaster simulation; multi-sensor geodetic fusion; climate-responsive risk modeling; satellite-based monitoring for early warning; urban and coastal resilience assessment; AI-driven multi-hazard analysis; and geospatial decision support systems for disaster management. 

Prof. Dr. Hong Sik Yun
Dr. Seung-Jun Lee
Guest Editors

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Keywords

  • geographic information systems (GIS)
  • disaster risk reduction
  • geospatial digital twin
  • multi-sensor data fusion
  • geodetic monitoring
  • remote sensing
  • climate-responsive hazard modeling
  • multi-hazard assessment
  • early warning systems
  • AI-driven geospatial analysis

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Published Papers (1 paper)

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Research

32 pages, 26175 KB  
Article
A High-Resolution LiDAR–GIS Framework for Riverine Flood Risk Prediction and Prevention Under Extreme Rainfall
by Seung-Jun Lee, Tae-Yun Kim, Jisung Kim and Hong-Sik Yun
Sustainability 2026, 18(7), 3390; https://doi.org/10.3390/su18073390 - 31 Mar 2026
Viewed by 580
Abstract
Riverine and pluvial flooding triggered by extreme monsoon rainfall is intensifying under climate change, yet flood-risk products in many coastal municipalities remain too coarse for parcel-scale prevention and climate-adaptive planning. This study presents a 1 m LiDAR–GIS flood susceptibility framework validated against consecutive [...] Read more.
Riverine and pluvial flooding triggered by extreme monsoon rainfall is intensifying under climate change, yet flood-risk products in many coastal municipalities remain too coarse for parcel-scale prevention and climate-adaptive planning. This study presents a 1 m LiDAR–GIS flood susceptibility framework validated against consecutive record-breaking floods in Dangjin City, South Korea (July 2024: 214.6 mm; July 2025: 377.4 mm). Five terrain parameters—elevation, slope, topographic wetness index, flow accumulation, and distance to stream—were integrated into a weighted Flood Susceptibility Index (FSI=0.20E^+0.30S^+0.25T^+0.15F^+0.10D^) and classified into four risk strata using K-means clustering (k = 4), identifying a high-risk zone of 0.3119 km2 (5.00% of the 6.18 km2 analysis domain). A Monte Carlo sensitivity analysis (n = 5000; ±0.10 weight perturbation) confirmed classification robustness (CV = 5.21%, mean Pearson r = 0.992). Static bathtub inundation scenarios (Δh = 0.5–2.0 m above the 5th-percentile baseline elevation of 13.29 m AMSL) produced footprint expansion from 0.370 to 0.572 km2, capturing all nine observed flood inventory points at the 2.0 m threshold, with flow-connectivity analysis confirming that 91.7–93.1% of predicted inundation is hydraulically connected to the D8-derived stream network. Spatial validation yielded a combined IoU of 6.51%, with a progressive increase from 3.33% (2024) to 6.50% (2025) confirming that the FSI correctly tracks flood-extent expansion with increasing rainfall intensity. Relying exclusively on topographic data and standard GIS algorithms, the framework supports scientifically grounded flood risk governance in data-limited municipalities, directly aligned with SDG 11, SDG 13, and Sendai Framework Target B. Full article
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