Prediction of Seismic Intensity Distributions Using Neural Networks
This addresses the challenge of seismic hazard assessment for earthquake-prone regions, but it is incremental as it builds on existing neural network and hybrid approaches.
The study tackled the problem of predicting seismic intensity distributions, especially abnormal ones affected by underground plate structures, by proposing a hybrid neural network model that treats distributions as 2D images, achieving accurate predictions.
The ground motion prediction equation is commonly used to predict the seismic intensity distribution. However, it is not easy to apply this method to seismic distributions affected by underground plate structures, which are commonly known as abnormal seismic distributions. This study proposes a hybrid of regression and classification approaches using neural networks. The proposed model treats the distributions as 2-dimensional data like an image. Our method can accurately predict seismic intensity distributions, even abnormal distributions.