rishabh-khurana / earthquake-prediction

To predict the time that an earthquake will occur in a laboratory test using Scikit-Learn (Pedregosa et al. (2011), XGBoost (Chen & Guestrin, 2016) and LightGBM (Ke, et al., 2017) libraries for machine learning and support. The laboratory test applies shear forces to a sample of earth and rock containing a fault line. If the physics are ultimately shown to scale from the laboratory to the field, researchers will have the potential to improve earthquake hazard assessments that could save lives and billions of dollars in infrastructure. The metric used is Mean Absolute Error (MAE) and thus a lower value is better with zero representing a perfect fit.

Earthquake-Prediction

To predict the time that an earthquake will occur in a laboratory test using Scikit-Learn (Pedregosa et al. (2011), XGBoost,CatBoost and LightGBM libraries for machine learning and support. The laboratory test applies shear forces to a sample of earth and rock containing a fault line. If the physics are ultimately shown to scale from the laboratory to the field, researchers will have the potential to improve earthquake hazard assessments that could save lives and billions of dollars in infrastructure. The metric used is Mean Absolute Error (MAE) and thus a lower value is better with zero representing a perfect fit.

To setup the project

In the project directory, create folder data_files/ and store the files train.csv, sample_submission.csv, and test/ folder containing segment_ids. These files can be downloaded from the link https://www.kaggle.com/c/LANL-Earthquake-Prediction/data. Also create result/ folder in this directory.

To install the dependencies:

Before running the actual code , we need to install all libraries used in the module using the following command:

pip3 install -r requirements.txt

To run the code

Type the following command to run the code:

python3 main.py

Which will run the code in the 'main.py' file , applies the machine learning model depending on the function called under the

__name__=__main__:

section of the main.py file.

Training Model Files

Our Implemention basically compares the accuracy of prediction of time to failure using different machine learning models , namely xgBoost, lightgbm and catBooster , which is implemented in the following files:

prediction_cbr.py -> CatBoost prediction_xgb.py -> xgBoost prediction_lgb.py -> lightgbm

Result Files

For each model, after the code runs to completion, four files are generated in the folder: data_files/result/ namely, train_features.csv which stores training data features, test_features.csv which stores test data features, time_to_failure.csv which stores actual time_to_failure values, and sample_submission.csv which stores predicted time_to_failure values.