Ground motion prediction using Artificial Neural Network in Pakistan

Abstract

The goal of this research project is to design, build, and validate an artificial neural network (ANN) model that predicts ground motion from the previous data of earthquake for seismic incidents in Pakistan. The prediction of ground motion is essential for determining the seismic risks and consequently providing measures to mitigate them. The ANN model implements activation function in hidden neurons to represent those relationships the seismic data holds and get logarithmic PGA values. The model performance evaluation metrics which are like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Correlation Coefficients prove the accuracy and robustness of the ANNPGA model. The ANNPGA model displays the best predictive power of all PGA values through the validation dataset. The MSE value is 0.00264 which model's accuracy in capturing ground motion variability. A comparative analysis with already created empirical and physics-based models will demonstrate that the ANPGA model gives more accurate predictions in most cases and especially in situations where nonlinear relationships are involved.