Risk Assessment of Bridge Damage Due to Heavy Rainfall Considering Landslide Risk and Driftwood Generation Potential Using Convolutional Neural Networks and Conventional Machine Learning

This study addresses the assessment of bridge damage risks associated with heavy rainfall, focusing on landslide susceptibility and driftwood generation potential. By integrating convolutional neural networks (CNNs) with traditional machine learning methods, the research develops an advanced predict...

Full description

Saved in:
Bibliographic Details
Published in:Water (Basel) 2024-12, Vol.16 (23), p.3471
Main Authors: Ren, Fudong, Isobe, Koichi, Ando, Miku
Format: Article
Language:English
Subjects:
Bridges
Disasters
Emergency management
Floods
Forests
Geospatial data
Infrastructure
Japan
Landslides
Landslides & mudslides
Machine learning
Natural disaster damage
Neural networks
Rain
Rain and rainfall
Risk assessment
Rivers
Roads & highways
Tourism
Trees
Typhoons
Vegetation
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This study addresses the assessment of bridge damage risks associated with heavy rainfall, focusing on landslide susceptibility and driftwood generation potential. By integrating convolutional neural networks (CNNs) with traditional machine learning methods, the research develops an advanced predictive framework for estimating driftwood accumulation at river bridges—a recognized challenge in disaster management. Concentrating on the Tokachi River basin in Hokkaido, Japan, the research utilizes diverse environmental and geographical data from authoritative sources. The findings demonstrate that the innovative approach not only enhances the accuracy of driftwood volume predictions but also distinguishes the effectiveness of CNNs compared to conventional methods. Crucially, areas prone to landslides are identified as significant contributors to driftwood generation, impacting bridge safety. The study underscores the potential of machine learning models in improving disaster risk assessment, while suggesting further exploration into real-time data integration and model refinement to adapt to changing climate conditions and ensure long-term infrastructure safety.
ISSN:2073-4441
2073-4441
DOI:10.3390/w16233471