A location-dependent earthquake prediction using recurrent neural network algorithms
In this paper, we propose a location-dependent earthquake prediction based on recurrent
neural network algorithms. The location-dependent prediction consists of clustering the …
neural network algorithms. The location-dependent prediction consists of clustering the …
An adaptable random forest model for the declustering of earthquake catalogs
F Aden‐Antoniów, WB Frank… - Journal of Geophysical …, 2022 - Wiley Online Library
Earthquake catalogs are essential to analyze the evolution of active fault systems. The
background seismicity rate, or rate of earthquakes that are not directly triggered by other …
background seismicity rate, or rate of earthquakes that are not directly triggered by other …
A communication, management and tracking mobile application for enhancing earthquake preparedness and situational awareness in the event of an earthquake
P Kirci, D Arslan, SF Dincer - Sustainability, 2023 - mdpi.com
The presented DepApp is an application that provides information about the intensity, time
and whereabouts of a recent earthquake. In addition, the presented application is a mobile …
and whereabouts of a recent earthquake. In addition, the presented application is a mobile …
Memory guided Aquila optimization algorithm with controlled search mechanism for seismicity analysis of earthquake prone regions
De-clustering the seismic catalog is one of the crucial processes in determining the
probability of exceeding ground motions at particular locations. Removing dependent …
probability of exceeding ground motions at particular locations. Removing dependent …
Intelligent solutions for earthquake data analysis and prediction for future smart cities
B Dey, P Dikshit, S Sehgal, V Trehan… - Computers & Industrial …, 2022 - Elsevier
The analysis and prediction of the Earthquake for smart cities are of greatest significance
because all the critical infrastructure like drinking water resources, mobile networks …
because all the critical infrastructure like drinking water resources, mobile networks …
Data-Driven Prediction of Seismic Intensity Distributions Featuring Hybrid Classification-Regression Models
K Mizutani, H Mitarai, K Miyazaki, S Kumano… - arXiv preprint arXiv …, 2024 - arxiv.org
Earthquakes are among the most immediate and deadly natural disasters that humans face.
Accurately forecasting the extent of earthquake damage and assessing potential risks can …
Accurately forecasting the extent of earthquake damage and assessing potential risks can …
Prediction of recovery time of infrastructure functionalities after an earthquake using machine learning
The recovery time (RT) is one of the essential components of infrastructure seismic
resilience analysis. This seismic infrastructure resilience is crucial to keep the functionality of …
resilience analysis. This seismic infrastructure resilience is crucial to keep the functionality of …
Transfer learning to build a scalable model for the declustering of earthquake catalogs
F Aden-Antoniow, WB Frank, L Seydoux - Authorea Preprints, 2022 - authorea.com
The rate of background seismicity, or the earthquakes not directly triggered by another
earthquake, in active seismic regions is indicative of the stressing rate of fault systems …
earthquake, in active seismic regions is indicative of the stressing rate of fault systems …
Transfer Learning for Detecting Fake Images that Resulted from Turkey Earthquake
JY Alzamily, SI Abudalfa - … in Technical and Vocational Education and …, 2024 - Springer
Abstract Background: On February 6, 2023, a devastating 7.8 magnitude earthquake struck
southern and central Turkey, as well as northern and western Syria, causing widespread …
southern and central Turkey, as well as northern and western Syria, causing widespread …
Performance Improvement in Time Series Prediction through PECNET Framework
S Macit, BB Üstündağ - 2023 International Conference on …, 2023 - ieeexplore.ieee.org
Machine learning algorithms have garnered con-siderable attention and recognition in the
context of addressing time series prediction challenges. However, constructing a ma-chine …
context of addressing time series prediction challenges. However, constructing a ma-chine …