Iran is one of the most seismic countries of the world. It is situated over the Himalayan-Alpide seismic belt and is one of those countries, which have lost many human lives and a lot of money due to the occurrence of earthquakes. Here a model is built using Machine Learning to predict PGA in this region.
The dataset was split into input part and the PGA value which was supposed to be predicted. The input part was standardised using StandardScalar. ● Now, the entire dataset was split into test set and training set. ● We trained the model using Logistic Regression, K-Nearest Neighbours(KNN) and Random Forest Regression using the training set. ● The trained models were tested with the test set to predict the efficiency of the model. ● Finally, two Ensemble models were created combining the previous models, one is the Averaging technique and the other is Blending technique. The efficiency of the models were predicted by calculating the Mean Average Error MAE , Mean Squared Error- MSE and Root Mean Squared Error- R MSE. Lower the value, the higher is the efficiency.
Iran is one of the most seismic countries of the world. It is situated over the Himalayan-Alpide seismic belt and is one of those countries, which have lost many human lives and a lot of money due to the occurrence of earthquakes. Here a model is built using Machine Learning to predict PGA in this region.
The dataset was split into input part and the PGA value which was supposed to be predicted. The input part was standardised using StandardScalar. ● Now, the entire dataset was split into test set and training set. ● We trained the model using Logistic Regression, K-Nearest Neighbours(KNN) and Random Forest Regression using the training set. ● The trained models were tested with the test set to predict the efficiency of the model. ● Finally, two Ensemble models were created combining the previous models, one is the Averaging technique and the other is Blending technique. The efficiency of the models were predicted by calculating the Mean Average Error MAE , Mean Squared Error- MSE and Root Mean Squared Error- R MSE. Lower the value, the higher is the efficiency.