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Artificial neural network approaches for disaster management

A literature review

Bibliographic Data

ID22027971
AuthorsSreeparna Guha, Rabin K Jana (0000-0001-8564-112X, Indian Institute of Management Raipur, corresponding author), Manas Kumar Sanyal (0000-0002-5760-8207, University of Kalyani)
Year2022
Volume81
Pages103276
Publication date2022-10-01
Open AccessYes
TypeARTICLE
VenueInternational Journal of Disaster Risk Reduction (JOURNAL)
Journal identifiersISSN: 2212-4209
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.ijdrr.2022.103276
OpenAlexW4293581732
LanguageEN
Citations received8
References cited128

Artificial neural network · Business · Convolutional neural network · Data science · Deep learning · Emergency management · Extant taxon · Flood myth · Geography · Hazard · Political science · Preparedness · Computer Science · Disaster Management and Resilience · Flood Risk Assessment and Management · Public Relations and Crisis Communication · Artificial Intelligence

  • Multi-source driven estimation of earthquake economic losses

    Open Access•Jinpeng Zhao, Xiaojun Li et al.•International Journal of Disaster…•2024

  • A High-Resolution Spatial Distribution-Based Integration Machine Learning Algorithm for Urban Fire Risk Assessment

    Open Access•Yulu Hao, Mengdi Li et al.•ISPRS International Journal of…•2023

  • Enhancing emergency vehicle access in dense settlements of Mumbai using high-resolution satellite imagery

    Open Access•Vivek Kumar Singh, Vaibhav Kumar•International Journal of Disaster…•2024

  • Multi-modal mining of crowd-sourced data

    Open Access•Sadegh Khanmohammadi, Emad Golafshani et al.•International Journal of Disaster…•2023

  • A text mining analytic approach for distinguishing between disaster and non-disaster zones from tweets

    Open Access•Steven Walczak, Ly Dinh•International Journal of Disaster…•2025

  • Explainable artificial intelligence in disaster risk management

    Open Access•Saman Ghaffarian, Firouzeh Taghikhah et al.•International Journal of Disaster…•2023

  • Transforming ground disaster response

    Open Access•Angelo J Soto-Vergel, Juan C Vélez et al.•International Journal of Disaster…•2023

  • On the emergence of geospatial cloud-based platforms for disaster risk management

    Open Access•Mirza Waleed, M Sajjad•International Journal of Disaster…•2023

  • ImageNet classification with deep convolutional neural networks

    Open Access•Alex Krizhevsky, Ilya Sutskever et al.•Communications of the ACM•2017

  • The self-organizing map

    Open Access•Teuvo Kohonen•Proceedings of the IEEE•1990

  • A Fast Learning Algorithm for Deep Belief Nets

    Geoffrey E Hinton, Simon Osindero et al.•Neural Computation•2006

  • Backpropagation Applied to Handwritten Zip Code Recognition

    Yann LeCun, B Boser et al.•Neural Computation•1989

  • Deep learning

    Open Access•Yann LeCun, Yoshua Bengio et al.•Nature•2015

  • Disaster Image Classification by Fusing Multimodal Social Media Data

    Open Access•Zhiqiang Zou, Hongyu Gan et al.•ISPRS International Journal of…•2021

  • A big data-driven dynamic estimation model of relief supplies demand in urban flood disaster

    Open Access•Anqi Lin, Hao Wu et al.•International Journal of Disaster…•2020

  • Improving disasters preparedness and response for coastal communities using AIS ship tracking data

    Open Access•Samsul Islam, Floris Goerlandt et al.•International Journal of Disaster…•2020

  • Earthquake risk assessment using an integrated Fuzzy Analytic Hierarchy Process with Artificial Neural Networks based on GIS

    Open Access•Peyman Yariyan, Hasan Zabihi et al.•International Journal of Disaster…•2020

  • A Machine Learning Ensemble Approach Based on Random Forest and Radial Basis Function Neural Network for Risk Evaluation of Regional Flood Disaster

    Open Access•Junfei Chen, Qian Li et al.•International Journal of…•2019

  • A deep learning-based social media text analysis framework for disaster resource management

    Open Access•Ashutosh Bhoi, Sthita Pragyan Pujari et al.•Social Network Analysis and Mining•2020

Citations per year2.67
Citation span2023 - 2025 (3)
Highly citedNo

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