Machine Learning Models Predicting Dangerous Seismic Events

This article was written by Michal Tadeusiak


Underground mining poses a number of threats including fires, methane outbreaks or seismic tremors and bumps. An automatic system for predicting and alerting against such dangerous events is of utmost importance – and also a great challenge for data scientists and their machine learning models. This was the inspiration for the organizers of AAIA’16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines.

Our solutions topped the final leaderboard by taking the first two places. In this post, we present the competition and describe our winning approach. For a more detailed description please see our publication.

The Challenge

The datasets provided by organizers comprised of seismic activity records from Polish coal mines (24 working sites) collected throughout a period of several months. The goal was to develop a prediction model that, based on recordings from a 24-hour period, would predict whether an energy warning level (50k Joules) was going to be exceeded in the following 8 hours.

The participants were presented with two datasets: one for training and the other for testing purposes. The training set consisted of 133,151 observations accompanied by labels – binary values indicating warning level excess. The test set was of a moderate size of 3,860 observations. The prediction model should automatically assess the danger for each event, i.e. evaluate the likelihood of exceeding the energy warning level for each observation.

The model’s quality was to be judged by the accuracy of the likelihoods measured with respect to the Area Under ROC curve (AUC) metric. This is a common metric for evaluating binary predictions, particularly when the classes are imbalanced (only about 2% of events in the training dataset were labeled as dangerous). The AUC measure can be interpreted as the probability that a randomly drawn observation from the danger class will have a higher likelihood assigned to it than a randomly drawn observation from the safe class. Knowing the interpretation it is easy to assess the quality of the results: a score of 1.0 means perfect prediction and 0.5 is the equivalent of random guessing. (Bonus question for the readers: what would a score of 0 mean?).

What you will find in this article:

  • Overview of the data
  • Our insights:

              -Sparse information
              -Multiple locations
              -Working site proxies

  • Features
  • Validation
  • Model selection
  • Model ensembling
  • Final results

To check out all this information, click here. For other articles about machine learning, click here

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