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Earthquake Damage Assessment in Three Spatial Scale Using Naive Bayes, SVM, and Deep Learning Algorithms

Sajjad Ahadzadeh, Mohammad Reza Malek
2021 Applied Sciences  
(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

Vasileios Linardos, Maria Drakaki, Panagiotis Tzionas, Yannis L. Karnavas
2022 Machine Learning and Knowledge Extraction  
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

Juan P. Amezquita-Sanchez, Martin Valtierra-Rodriguez, Mais Aldwaik, Hojjat Adeli
2016 Scientia Iranica. International Journal of Science and Technology  
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

Mohammad Amin Nabian, Hadi Meidani
2018 Computer-Aided Civil and Infrastructure Engineering  
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

Takashi Miyamoto, Yudai Yamamoto
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
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

Peter Damilola Ogunjinmi, Sung-Sik Park, Bubryur Kim, Dong-Eun Lee
2022 Sensors  
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

Shira Daskal, Adar Ben-Eliyahu, Gal Levy, Yakov Ben-Haim, Ronnen Avny
2022 Sustainability  
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]

Firoj Alam, Tanvirul Alam, Muhammad Imran, Ferda Ofli
2021 arXiv   pre-print
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

Maja Kucharczyk, Chris H. Hugenholtz
2021 Remote Sensing of Environment  
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

Kevin Fauvel, Daniel Balouek-Thomert, Diego Melgar, Pedro Silva, Anthony Simonet, Gabriel Antoniu, Alexandru Costan, Véronique Masson, Manish Parashar, Ivan Rodero, Alexandre Termier
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
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

Priyanka Singh, Umaid Faraz Ahmad, Siddharth Yadav, A. Fedyukhin, S. Dixit
2020 E3S Web of Conferences  
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]

Mansi Agarwal, Maitree Leekha, Ramit Sawhney, Rajiv Ratn Shah, Rajesh Kumar Yadav, Dinesh Kumar Vishwakarma
2020 Lecture Notes in Computer Science  
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]

Ferda Ofli, Firoj Alam, Muhammad Imran
2020 arXiv   pre-print
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]

Fahim Faisal Niloy, Arif, Abu Bakar Siddik Nayem, Anis Sarker, Ovi Paul, M. Ashraful Amin, Amin Ahsan Ali, Moinul Islam Zaber, AKM Mahbubur Rahman
2021 arXiv   pre-print
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

Yasuno Takato, Amakata Masazumi, Fujii Junichiro, Shimamoto Yuri
2018 Figshare  
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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