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THE EXTRACTION OF POST-EARTHQUAKE BUILDING DAMAGE INFORMATIOM BASED ON CONVOLUTIONAL NEURAL NETWORK
Earthquake Seismic Damage Information Extraction Deep Learning Convolutional Neural Network
2018/5/11
The seismic damage information of buildings extracted from remote sensing (RS) imagery is meaningful for supporting relief and effective reduction of losses caused by earthquake. Both traditional pixe...
A new approach for residual gravity anomaly profile interpretations: Forced Neural Network (FNN)
Forced Neural Network gravity anomaly modeling synthetic model Gulf of Mexico
2015/9/8
This paper presents a new approach for interpretation of residual gravity anomaly profiles, assuming horizontal
cylinders as source. The new method, called Forced Neural Network (FNN), is introduced ...
Assessment of earthquake-triggered landslide susceptibility in El Salvador based on an Artificial Neural Network model
Assessment earthquake-triggered landslide susceptibility El Salvador based Artificial Neural Network model
2010/10/20
This paper presents an approach for assessing earthquake-triggered landslide susceptibility using artificial neural networks (ANNs). The computational method used for the training process is a back-pr...
Neural-network-based prediction techniques for single station modeling and regional mapping of the foF2 and M(3000)F2 ionospheric characteristics
Neural-network-based prediction techniques single station modeling regional mapping ionospheric characteristics
2009/11/12
In this work, Neural-Network-based single-station hourly daily foF2 and M(3000)F2 modelling of 15 European ionospheric stations is investigated. The data used are neural networks and hourly daily valu...
Neural network prediction of geomagnetic activity:a method using local Hölder exponents
Neural network prediction geomagnetic activity local Hö lder exponents
2009/11/12
Local scaling and singularity properties of solar wind and geomagnetic time series were analysed using Hölder exponents . It was shown that in analysed cases due to the multifractality of fluctua...
Total ozone time series analysis:a neural network model approach
Total ozone time series analysis neural network model approach
2009/11/11
This work is focused on the application of neural network based models to the analysis of total ozone (TO) time series. Processes that affect total ozone are extremely non linear, especially at the co...
An artificial neural network predictor for tropospheric surface duct phenomena
artificial neural network predictor tropospheric surface duct phenomena
2009/11/3
In this work, an artificial neural network (ANN) model is developed and used to predict the presence of ducting phenomena for a specific time, taking into account ground values of atmospheric pressure...