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Updated: Aug 27, 2025

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Resilient landscape pattern for reducing coastal flood susceptibility.

Ziyuan Luo1, Jian Tian1, Jian Zeng2

  • 1School of Architecture, Tianjin University, Tianjin 300072, PR China.

The Science of the Total Environment
|October 1, 2022
PubMed
Summary

This study maps flood susceptibility in Xiamen using neural networks and landscape metrics. Key findings reveal specific landscape patterns that increase or decrease flood risk, informing urban planning for resilience.

Keywords:
Flood resilienceGreen infrastructure networksLandscape metricsMachine learningNature-based solutionsSpatial planning

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Area of Science:

  • Environmental Science
  • Urban Planning
  • Geospatial Analysis

Background:

  • Flood susceptibility assessment and landscape pattern analysis are crucial for sustainable urban development and effective flood mitigation.
  • Identifying flood-prone areas and understanding their relationship with urban landscapes are essential for coastal city resilience.

Purpose of the Study:

  • To evaluate flood susceptibility in Xiamen using a neural network model and identify key landscape pattern metrics influencing flood risk.
  • To propose urban spatial planning strategies for flood management based on the ecological significance of selected landscape metrics.

Main Methods:

  • Flood susceptibility was assessed using a neural network model based on a satellite-derived inundation map (2010-2020) and explanatory factors identified by Geodetector and regularized random forest.
  • Landscape patterns were quantified, and key metrics at patch and class levels were statistically selected for their relationship with flood susceptibility.
  • Statistical approaches were used to select significant landscape pattern metrics influencing flood susceptibility.

Main Results:

  • Flood-prone areas in Xiamen are concentrated along riverbanks and coastlines.
  • Patch-level landscape metrics demonstrated greater explanatory power for flood susceptibility than class-level metrics.
  • Specific metrics like forest division index, water connectance index, urban core area number, urban fractal dimension index, and urban/water Euclidean nearest-neighbor distance positively correlated with flood susceptibility.

Conclusions:

  • Urban planning should integrate intensive strategies and Nature-based Solutions networks to enhance coastal flood resilience.
  • Understanding the interplay between landscape patterns and flood susceptibility is vital for developing effective urban flood management strategies.
  • Selected landscape metrics, particularly at the patch level, provide significant insights into flood risk assessment and urban planning.