Machine Learning Applications in Natural Disastermanagement
DOI:
https://doi.org/10.54097/zx4yng38Keywords:
Machine learning; algorithm; disaster management.Abstract
In the recent century, humans have been using fossil fuels without restraint, causing significant changes in the Earth's atmospheric environment, resulting in increased intensity and frequency of extreme weather/climate events and natural disasters. Natural disasters bring enormous risks and economic losses to human society, and natural disasters are a major challenge that human society have to face. Applying the scientific technology to predict, monitor, evaluate and manage the natural disasters has become a concern for governments, academia, and the general public all over the world. In recent years, the remarkable improvement of science and technology lead to a great concern of the application of machine learning technology of monitoring and managing the natural disasters Based on literature retrieval of professional databases. This article review some research papers related in model algorithms of machine learning, application status and future research directions of machine learning applications in natural disasters such as earthquakes, geological disasters, meteorological disasters.
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[1] Giardina G, Macchiarulo V, Foroughnia F, et al. Combining remote sensing techniques and field surveys for post-earthquake reconnaissance missions. Bulletin of Earthquake Engineering, 2024, 22(7): 3415-3439.
[2] Chittora P, Chakrabarti T, Debnath P, et al. Experimental analysis of earthquake prediction using machine learning classifiers, curve fitting, and neural modeling. 2022.
[3] Oktarina R, Bahagia S N, Diawati L, et al. Artificial neural network for predicting earthquake casualties and damages in Indonesia//IOP Conference Series: Earth and Environmental Science. IOP Publishing, 2020, 426(1): 012156.
[4] DARPITO K. Earthquake damage assessment: application and verification of the radius method to yogyakarta earthquake 2006. Bulletin of the International Institute of Seismology and Earthquake Engineering, 2011, 45: 85-90.
[5] Badal J, Vazquez-Prada M, González Á. Preliminary quantitative assessment of earthquake casualties and damages. Natural Hazards, 2005, 34: 353-374.
[6] Yang D H, Zhou X, Wang X Y, et al. Mirco-earthquake source depth detection using machine learning techniques. Information Sciences, 2021, 544: 325-342.
[7] Bian Y, Chen H, Liu Z, et al. Geological Disaster Susceptibility Evaluation Using Machine Learning: A Case Study of the Atal Tunnel in Tibetan Plateau. Sustainability, 2024, 16(11): 4604.
[8] Li R, Tan S, Zhang M, et al. Geological Disaster Susceptibility Evaluation Using a Random Forest Empowerment Information Quantity Model. Sustainability, 2024, 16(2): 765.
[9] Li C, Han J, Wu C, et al. Volcanic disaster scene classification of remote sensing image based on deep multi-instance network. Acta Geophysica, 2024: 1-17.
[10] Tan L. Research on Economic Loss Assessment of Urban Rainstorm Flood Disasters-Based on the Perspective of Data Fusion. 2022.
[11] Razali N, Ismail S, Mustapha A. Machine learning approach for flood risks prediction. IAES International Journal of Artificial Intelligence, 2020, 9(1): 73.
[12] Yang H, Yao R, Dong L, et al. Advancing flood susceptibility modeling using stacking ensemble machine learning: A multi-model approach. Journal of Geographical Sciences, 2024, 34(8): 1513-1536.
[13] Motta M, de Castro Neto M, Sarmento P. A mixed approach for urban flood prediction using Machine Learning and GIS. International journal of disaster risk reduction, 2021, 56: 102154.
[14] Chen J, Hill A A, Urbano L D. A GIS-based model for urban flood inundation. Journal of Hydrology, 2009, 373(1-2): 184-192.
[15] Kızıloluk S, Sert E. Hurricane-Faster R-CNN-JS: Hurricane detection with faster R-CNN using artificial Jellyfish Search (JS) optimizer. Multimedia Tools and Applications, 2022, 81(26): 37981-37999.
[16] Anyidoho P K, Ju X, Davidson R A, et al. A machine learning approach for predicting hurricane evacuee destination location using smartphone location data. Computational Urban Science, 2023, 3(1): 30.
[17] Wu K, Wu J, Ye M. A review on the application of social media data in natural disaster emergency management. Prog. Geogr, 2020, 39: 1412-1422.
[18] Yang L, Cervone G. Analysis of remote sensing imagery for disaster assessment using deep learning: a case study of flooding event. Soft Computing, 2019, 23(24): 13393-13408.
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