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Earthquake Damage Assessment in Three Spatial Scale Using Naive Bayes, SVM, and Deep Learning Algorithms
2021
Applied Sciences
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(SVM), and deep learning classification algorithms. ...
Based on the result of the validation of the estimated damage map with official data, the SVM performed better for damage estimation, followed by deep learning. ...
In this study, disaster-related messages during and after the earthquake were used for damage estimation at three scales. ...
doi:10.3390/app11209737
fatcat:4iw5loq2gve6tp6dgu4pb3kxjy
Machine Learning in Disaster Management: Recent Developments in Methods and Applications
2022
Machine Learning and Knowledge Extraction
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Recent developments in artificial intelligence (AI) and especially in machine learning (ML) and deep learning (DL) have been used to better cope with the severe and often catastrophic impacts of disasters ...
In particular, focus has been given on studies in the areas of disaster and hazard prediction, risk and vulnerability assessment, disaster detection, early warning systems, disaster monitoring, damage ...
"case studies", "applications", "machine learning", "deep learning". ...
doi:10.3390/make4020020
fatcat:wcdrh23k5ja6tdqlyhl7erobey
Neurocomputing in Civil Infrastructure
2016
Scientia Iranica. International Journal of Science and Technology
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Deep machine learning techniques are among the newest techniques to nd applications in civil infrastructure systems. journal. ...
The most common ANN used in structural engineering is the backpropagation neural network followed by recurrent neural networks and radial basis function neural networks. ...
[124] presented deep learningbased crack damage detection using convolutional neural network. Other applications of deep neural network learning model should follow. ...
doi:10.24200/sci.2016.2301
fatcat:f35gtppgofaojkbertgsowsi24
Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks
2018
Computer-Aided Civil and Infrastructure Engineering
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2017 pre-print arXiv:1708.08551v1 fatcat:oeuh3pvdrjdl3nvgzdej5syx7m
This paper presents a deep learning framework for accelerating infrastructure system reliability analysis. ...
While numerous research efforts have addressed and quantified the impact of natural disasters on infrastructure systems, typically using the Monte Carlo approach, they still suffer from high computational ...
Acknowledgement This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1053575. ...
doi:10.1111/mice.12359
fatcat:r4pag7aqijgxhe4akpvd4bsw5e
Using 3D Convolution and Multimodal Architecture For Earthquake Damage Detection Based on Satellite Imagery and Digital Urban Data
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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The damage detection system is designed as a deep learning model that uses multimodal data, consisting of optical satellite images and structural attributes. ...
When a large earthquake occurs, it is quite important to quickly figure out the damage distribution of housing structures for disaster prevention measures. ...
We express our sincere condolences to the victims of the earthquake and wish for a quick recovery and reconstruction of the affected area. ...
doi:10.1109/jstars.2021.3102701
fatcat:iz4katspnza2nom3ruw3byorki
Rapid Post-Earthquake Structural Damage Assessment Using Convolutional Neural Networks and Transfer Learning
2022
Sensors
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Herein, we present the effectiveness of automated deep learning in enhancing the assessment of damage caused by the 2017 Pohang earthquake. ...
The severity of damage is quantified by assigning damage assessment values, derived using the CNN model and gradient-weighted class activation mapping. ...
The automated deep learning (DL) method may be critical for enabling the rapid real-time detection and classification of structural damage (SD) attributed to earthquakes. ...
doi:10.3390/s22093471
pmid:35591163
pmcid:PMC9099597
fatcat:puo7avkplbajllxuj5pdqzcrxu
Earthquake Vulnerability Reduction by Building a Robust Social-Emotional Preparedness Program
2022
Sustainability
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Despite the progress made in understanding the characteristics of earthquakes, the predictions of earthquake activity are still inevitably very uncertain, mainly because of the highly complex nature of ...
the earthquake process. ...
Unlike a seismic level 7 earthquake, during a seismic level 9 earthquake, the long-term damage to infrastructure and buildings will lead to highly restricted accessibility due to damage to road infrastructure ...
doi:10.3390/su14105763
fatcat:pqiovf5n7nfithqo42qz6ga7ly
Robust Training of Social Media Image Classification Models for Rapid Disaster Response
[article]
2021
arXiv
pre-print
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We also explore various data augmentation strategies, semi-supervised techniques, and a multitask learning setup. In our extensive experiments, we achieve promising results. ...
irrelevant images, classifying images into specific humanitarian categories, and assessing the severity of the damage. ...
Both images show severe infrastructure damage.
Fig. 4 : 4 Number of images shared during 2015 Nepal Earthquake. ...
arXiv:2104.04184v2
fatcat:rcvrxscwtnfb7lhis5p2q4c2mi
Remote sensing of natural hazard-related disasters with small drones: Global trends, biases, and research opportunities
2021
Remote Sensing of Environment
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Small (< 25 kg) aerial drones have expanded the remote sensing toolkit for disaster management activities. ...
We performed a systematic literature search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology, resulting in 635 relevant articles from which we derived statistics ...
deep learning that use drone data to train classification algorithms. ...
doi:10.1016/j.rse.2021.112577
fatcat:wwtgi5bcgjfqtezcv7v44hqgoe
A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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EEW systems are designed to detect and characterize medium and large earthquakes before their damaging effects reach a certain location. ...
Our research aims to improve the accuracy of Earthquake Early Warning (EEW) systems by means of machine learning. ...
Finally, deep learning methods use Long-Short Term Memory (LSTM) and/or Convolutional Neural Networks (CNN) to extract latent features. ...
doi:10.1609/aaai.v34i01.5376
fatcat:rlrhesacb5gbjff3jg6thqfuny
Structural Health Monitoring and Damage Detection through Machine Learning approaches
2020
E3S Web of Conferences
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SHM implements a technique for damage detection and classification, including data from a system collected under different structural states using a piezoelectric sensor network using guided waves, hierarchical ...
non-linear primary component analysis and machine learning. ...
To identify damage Deep neural convolution network with hydro-connection learning transmission was introduced. Figure 2 explains the convolution process. ...
doi:10.1051/e3sconf/202022001096
fatcat:2xt25padfjfdrlgxyahgnoqlye
MEMIS: Multimodal Emergency Management Information System
[chapter]
2020
Lecture Notes in Computer Science
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We present MEMIS, a system that can be used in emergencies like disasters to identify and analyze the damage indicated by user-generated multimodal social media posts, thereby helping the disaster management ...
Different modalities often present supporting facts about the task, and therefore, learning them together can enhance performance. ...
As input to the deep learning models, we use 100-dimensional Fasttext word embeddings [6] trained on the dataset. ...
doi:10.1007/978-3-030-45439-5_32
fatcat:tnhslpgcivgrrhmwlpterv2yeq
Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response
[article]
2020
arXiv
pre-print
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In this paper, we propose to use both text and image modalities of social media data to learn a joint representation using state-of-the-art deep learning techniques. ...
The types of information shared include reports of injured or deceased people, infrastructure damage, and missing or found people, among others. ...
For example, use images shared on Twitter to assess the severity of infrastructure damage. Mouzannar et al. 2018 also focus on identifying damages in infrastructure and environmental elements. ...
arXiv:2004.11838v1
fatcat:t46ww2jm2zauffn2g7fdkhef6u
A Novel Disaster Image Dataset and Characteristics Analysis using Attention Model
[article]
2021
arXiv
pre-print
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2021
2020 25th International Conference on Pattern Recognition (ICPR)
unpublished
doi:10.1109/icpr48806.2021.9412504 fatcat:kczhiv7vuzfqfkttal2zyr3tvq
doi:10.1109/icpr48806.2021.9412504 fatcat:kczhiv7vuzfqfkttal2zyr3tvq
The advancement of deep learning technology has enabled us to develop systems that outperform any other classification technique. ...
Besides this, we have also collected images for various damaged infrastructure due to natural or man made calamities and damaged human due to war or accidents. ...
[10] proposed a multimodal deep learning framework to identify damage related information from social media posts. ...
arXiv:2107.01284v1
fatcat:oidxvtl77ne4rk4txrdq27w5xu
DISASTER INITIAL RESPONSES MINING DAMAGES USING FEATURE EXTRACTION AND BAYESIAN OPTIMIZED SUPPORT VECTOR CLASSIFIERS
2018
Figshare
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The initial response to future earthquakes is an important issue related to knowledge of natural disasters and to predict the degree of damage to infrastructure using multi-mode usable data sources. ...
This paper proposes a feature extraction damage classification model using disaster images with five classes of damage after the occurrence of a huge earthquake. ...
Machine and Learning, and Deep Learning. ...
doi:10.6084/m9.figshare.7392518
fatcat:ilrbdd5wyzhtzp66afartuypxm
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