Earthquake Prediction


As the motivation behind earthquake prediction is to empower crisis measures to decrease demise and devastation, inability to give notice of a significant earthquake that happens, or possibly a satisfactory assessment of the hazard, can bring about legitimate risk, or even political cleansing.


Dataset contains 2 columns as below:

  • Acoustic_data - Acoustic wave reading
  • Time_to_failure - Time remaining before the next earthquake
Stock Forecasting

Random Forest Regression Workflow for Earthquake Prediction

Random Forest Regression model belongs to family of bagging regression. It is a supervised learning model that uses ensemble learning method for regression. Ensemble learning method is a technique that combines predictions from multiple models to make prediction more accurately than a single model.

Features of Random Forest -

  • Aggregates many decision trees
  • Prevents overfitting
Stock Forecasting

Prepare data for modeling

Follow workflow arrow

  • ZipWithIndex- Creates new feature column from dataframe index as ID
  • Group data- Creates new feature column as key obtained by ID divided by length of data
Stock Forecasting
  • Feature Engineering- Groups by data on key to create all statistical measures (min, max, mean, quartiles etc) as new feature
Stock Forecasting
  • Feature Vector - Merge multiple columns to form vector
Stock Forecasting

Data modeling

  • Before we create Random Forest Regression model, split data (80:20) into train and test for performance evaluation.

Random Forest Regression

  • Sets feature vector corresponding to label(time_to_failure_label).
  • Sets number of features for each split node of tree.
  • For regression the measure of impurity is variant.
  • In random forest, the impurity decrease from each feature can be averaged across trees to determine the final importance of the variable.
  • The maxBins signifies the maximum number of bins used for splitting the features, where the suggested value is 100 to get better results.
  • The maxDepth is the maximum depth of the tree (for example, depth 0 means one leaf node, depth 1 means one internal node plus two leaf nodes).
  • Information gain is calculated by comparing the entropy of the dataset before and after a transformation.
Stock Forecasting
Stock Forecasting

Model evaluation

  • Multiple ways to evaluate regression model such as R square, Root mean square error(rmse), mean square error(mse)
Stock Forecasting