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A Zoning Earthquake Casualty Prediction Model Based on Machine Learning

Boyi Li, Adu Gong, Tingting Zeng, Wenxuan Bao, Can Xu, Zhiqing Huang
2021 Remote Sensing  
To obtain a precise casualty prediction method that can be applied to regions with different geographical environments, a spatial division method based on regional differences and a zoning casualty prediction  ...  The results show that Z-SVR outperformed the other machine learning methods and can further enhance the accuracy of casualty prediction.  ...  Acknowledgments: The authors would like to express deep gratitude to Jianghong Zhao from Beijing University of Civil Engineering and Architecture for her guidance on the framework design of the paper.  ... 
doi:10.3390/rs14010030 fatcat:z3k7ejvsvbdhzpbwa2ei7hf6ny

Study on the Uncertainty of Machine Learning Model for Earthquake-Induced Landslide Susceptibility Assessment

Haixia Feng, Zelang Miao, Qingwu Hu
2022 Remote Sensing  
The landslide susceptibility assessment based on machine learning can accurately predict the probability of landslides happening in the region.  ...  However, there are uncertainties in machine learning applications.  ...  Support Vector Machine Model The Support Vector Machine (SVM) is a supervised learning approach for nonlinear covariable transformation based on statistical learning theory.  ... 
doi:10.3390/rs14132968 doaj:85f6bccb5dca4bb985327beceb9c022a fatcat:igcl62biyjcyvk4jwt6nxnmnna

An Earthquake Fatalities Assessment Method Based on Feature Importance with Deep Learning and Random Forest Models

Hanxi Jia, Junqi Lin, Jinlong Liu
2019 Sustainability  
This study aims to analyze and compare the importance of feature affecting earthquake fatalities in China mainland and establish a deep learning model to assess the potential fatalities based on the selected  ...  Finally, we proposed a model for estimating earthquake fatalities based on the seismic data from 1992 to 2017 in China mainland.  ...  A deep learning model for estimating human losses based on the results of the Random Forest algorithm was established.  ... 
doi:10.3390/su11102727 fatcat:6smocplgnzfu5ikxo53krkl7me

Hazard Assessment of Earthquake Disaster Chains Based on Deep Learning—A Case Study of Mao County, Sichuan Province

Yulin Su, Guangzhi Rong, Yining Ma, Junwen Chi, Xingpeng Liu, Jiquan Zhang, Tiantao Li
2022 Frontiers in Earth Science  
Therefore, this study aims to establish a DNN model suitable for the study of earthquake–geological disaster chains.  ...  Therefore, it is proved that the model can be applied for the prediction of chain disasters and is a promising tool for geological hazard assessment.  ...  Support Vector Machines (SVM) Model SVM is a machine learning method based on statistical learning theory that transforms original input space into a higherdimensional feature space to find an optimal  ... 
doi:10.3389/feart.2021.683903 fatcat:cage2i2qxbgjjb5cvqo6mnfa6m

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

Rabia Tehseen, Muhammad Shoaib Farooq, Adnan Abid
2020 Sustainability  
The article discusses different variants of rule-based, fuzzy, and machine learning based expert systems for earthquake prediction.  ...  Earthquake is one of the most hazardous natural calamity. Many algorithms have been proposed for earthquake prediction using expert systems (ES).  ...  Acknowledgments: The authors appreciate the anonymous reviewers for their valuable feedback on the initial version of this paper. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/su12062420 fatcat:bba37to45ram5okpe2aoryvxru

Long-Term Forecasting of Strong Earthquakes in North America, South America, Japan, Southern China and Northern India With Machine Learning

Victor Manuel Velasco Herrera, Eduardo Antonio Rossello, Maria Julia Orgeira, Lucas Arioni, Willie Soon, Willie Soon, Graciela Velasco, Laura Rosique-de la Cruz, Emmanuel Zúñiga, Carlos Vera
2022 Frontiers in Earth Science  
Then a series of earthquakes can occur along the fault until the stress subsides and a new cycle begins. Our machine learning models predict a new period of stron [...]  ...  From the information and pattern we obtained from the wavelet analyses, we created a probabilistic, long-term earthquake prediction model for each seismic zone using the Bayesian Machine Learning method  ...  We then created a probabilistic earthquake prediction model for each seismic zone analyzed using the Bayesian Machine Learning method.  ... 
doi:10.3389/feart.2022.905792 doaj:a2916e35227243aeb9cc76993b46bdf2 fatcat:qf4ruqruwbftpeggv3ospzkv64

A Hybrid Intelligent Model for Urban Seismic Risk Assessment from the Perspective of Possibility and Vulnerability Based on Particle Swarm Optimization

Jinlong Chu, Qiang Zhang, Ai Wang, Haoran Yu
2021 Scientific Programming  
We applied this model to Hefei, one of the few superlarge provincial capital cities on the "Tancheng-Lujiang" fault zone, one of the four major earthquake zones in China, which suffers frequent earthquakes  ...  on a variety of intelligent algorithms to develop a hybrid intelligent model that integrates probability and vulnerability to evaluate and quantify the difference in the urban spatial units distribution  ...  Predicting Seismic Probability. e rapid development of computer technology represented by machine learning provides new ideas for the detection of seismic events. e main content of machine learning research  ... 
doi:10.1155/2021/2218044 doaj:8af7325e760e4f34bd316da53d070cdf fatcat:fos33f4ubnea5pvtqjfnuydl4e

Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping

Xin Yang, Rui Liu, Mei Yang, Jingjue Chen, Tianqiang Liu, Yuantao Yang, Wei Chen, Yuting Wang
2021 Remote Sensing  
This study proposed a new hybrid model based on the convolutional neural network (CNN) for making effective use of historical datasets and producing a reliable landslide susceptibility map.  ...  To evaluate the validity of the proposed model, two pure CNN models and the previously used methods of random forest and a support vector machine were selected as the benchmark models.  ...  The authors also would like to thank Kun Chen from the Institute of Geophysics, China Earthquake Administration for sharing the PGA data.  ... 
doi:10.3390/rs13112166 fatcat:vxhukghotzhpnctlw2v3vfvuxm

Sunda-arc seismicity: continuing increase of high-magnitude earthquakes since 2004 [article]

Nishtha Srivastava, Omar El Sayed, Megha Chakraborty, Jonas Köhler, Jan Steinheimer, Johannes Faber, Alexander Kies, Kiran Kumar Thingbaijam, Kai Zhou, Georg Ruempker, Horst Stoecker
2021 arXiv   pre-print
Models with predictive or forecasting power are still lacking.  ...  Based on the data, we report a hitherto unreported strong increase in seismicity during the last two decades associated with strong earthquakes with mb greater than or equal to 6.5.  ...  Machine Learning and Deep Learning based methods may become the key to fostering these efforts. eastern side, active deformation takes place within a complex Suture Zone (linear belt of intense deformation  ... 
arXiv:2108.06557v1 fatcat:zbhrfyunpnbwbmprzwzxvhcpre

Forecast of Large Earthquake Emergency Supplies Demand Based on PSO-BP Neural Network

2022 Tehnički Vjesnik  
Then we predict the mortality rate and injury rate of a large earthquake. Hence the number of casualties and survivors can be obtained.  ...  A large earthquake will cause serious casualties, and a large number of emergency supplies will be needed in the disaster area.  ...  Acknowledgments This work is supported by Beijing Logistics Informatics Research Base.  ... 
doi:10.17559/tv-20211120092137 fatcat:ujpm65vqrvht7gsgzk4mjyexmq

Predicting Change in Adaptation Strategies of Households to Geological Hazards in the Longmenshan Area, China Using Machine Learning and GIS

Haichuan Su, Glenn Fernandez, Xiaoxi Hu, Shaolin Wu, Baofeng Di, Chunping Tan
2022 Water  
This exploratory study provides a glimpse of how machine learning can be utilized to predict how adaptation strategies would be modified if hazard frequency changed.  ...  Different post-disaster adaptation strategies of households in Longmenshan Town, Sichuan, China were identified through a questionnaire survey and then clustered into groups based on similarity using a  ...  The GBDT model is a widely used approach in machine learning based on establishing various weak classifiers for accumulation to a strong classifier with diverse iterations that improve the accuracy of  ... 
doi:10.3390/w14071023 fatcat:wwfmvqeo6jd3nbwnv4jkkpuue4

Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings

Vandana Kumari, Ehsan Harirchian, Tom Lahmer, Shahla Rasulzade
2022 Buildings  
Artificial intelligence (AI) methods, such as machine learning (ML) algorithm-based methods, are increasingly used in various scientific and technical applications.  ...  In addition, a web-based application built on Django is introduced. The interface is designed with the idea to ease the seismic vulnerability investigation in real-time.  ...  57 45 52 47 Table 5 . 5 Tested Predictions based on Nepal Earthquake Data.  ... 
doi:10.3390/buildings12050578 fatcat:wx6owpemgvg57hbo4nwfwps3n4

Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping

Rui Liu, Xin Yang, Chong Xu, Liangshuai Wei, Xiangqiang Zeng
2022 Remote Sensing  
Results suggest that both CNN and conventional machine learning-based models have a satisfactory performance.  ...  The objective of this study is to introduce a CNN-based model in LSM and systematically compare its overall performance with the conventional machine learning models of random forest, logistic regression  ...  Additionally, we only used the typical models and network architecture and did not combine engineering geology analysis methods into the model.  ... 
doi:10.3390/rs14020321 fatcat:en6ktpo5sbbjhazo5fttdb24ue

A CNN-BiLSTM Model with Attention Mechanism for Earthquake Prediction [article]

Parisa Kavianpour, Mohammadreza Kavianpour, Ehsan Jahani, Amin Ramezani
2021 arXiv   pre-print
Aware of these issues, this paper proposes a novel prediction method based on attention mechanism (AM), convolution neural network (CNN), and bi-directional long short-term memory (BiLSTM) models, which  ...  can predict the number and maximum magnitude of earthquakes in each area of mainland China-based on the earthquake catalog of the region.  ...  They machine learning approaches in predicting earthquake.  ... 
arXiv:2112.13444v1 fatcat:hlmge5ezpzec3d6q6xc5xcdcbm

Spatiotemporally explicit earthquake prediction using deep neural network

Mohsen Yousefzadeh, Seyyed Ahmad Hosseini, Mahdi Farnaghi
2021 Soil Dynamics and Earthquake Engineering  
Due to the complexity of predicting future earthquakes, machine learning algorithms have been used by several researchers to increase the Accuracy of the forecast.  ...  This study introduces and investigates the effect of spatial parameters on four ML algorithms' performance for predicting the magnitude of future earthquakes in Iran as one of the most earthquake-prone  ...  Machine learning algorithms SVM is a supervised learning method based on statistical learning theory and the structural risk minimization principle [57] .  ... 
doi:10.1016/j.soildyn.2021.106663 fatcat:gjvwj3joljbpzdf4dk5k25tl4y
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