1. Predicting Earthquakes By Lois Desplat

2. Why Predict Earthquakes?  To minimize the loss of life and property.  Unfortunately, current techniques do not have a high enough accuracy to be able to accurately predict earthquakes.

3. Estimating earthquake probabilities  Scientists study the histories of large earthquakes in a specific area  The rate at which strain accumulates in the rock

4. Methods to earthquake prediction  Need to construct models based on: Partial differential equations Partial differential equations Finite automata Finite automata Supervised learning techniques: Supervised learning techniques: Decision TreeDecision Tree Bayesian ClassificationBayesian Classification Feed-Forward Neural NetworksFeed-Forward Neural Networks

5. Decision Tree  Tries to generate rules with high accuracy  ID3, …

6. Bayesian Classifiers  They are statistical classifiers  Only needs a small sample to find the means and variances of the variables necessary for classification  It can find the probability that a given sample belongs to a certain class (earthquake > 3.0)  Uses Bayes Theorem

7. Feed-Forward Neural Network  Network given a set of input and respective output to start learning  It connects each Perceptron and the algorithm tries to minimize the weigths between Perceptrons to the minimum so that the input give the right output

8. The Bagging Method  Combine the predictions of the past three algorithms  You get a much more accurate prediction  Give different learning samples to each algorithm

9. Some Problems  The Data can have a lot of extra information that adds noise i.e. We might not want small scale earthquakes that are really just aftershocks of big earthquakes  We only look at the data in 1 dimension, maybe if we plot the data in multiple dimensions, we might some patterns

10. Not Good Enough!  Authors claim that their bagging method has 92% accuracy.  Highly doubt accuracy of that number but even if true: We still cannot predict earthquakes with enough confidence We still cannot predict earthquakes with enough confidence

11. Solution  Do short-term predictions instead of long- term  Analyze the data in multiple dimensions over space, time and feature space.

13. Visualization of the Data Space

15.  Data Space uses Magnitude, Epicentral Coordinate, Depth and Time of occurrence  7D space uses: NS: Degree of spatial non-randomness at short distances NS: Degree of spatial non-randomness at short distances LS: Degree of spatial non-randomness at long distances LS: Degree of spatial non-randomness at long distances CD: Spatial correlation dimension CD: Spatial correlation dimension SR: Degree of spatial repetitiveness SR: Degree of spatial repetitiveness AZ: Average Depth AZ: Average Depth TI: Time Interval for the occurance of 100 events in the sample space. TI: Time Interval for the occurance of 100 events in the sample space. MR: Ratio of two events falling into different magnitude ranges MR: Ratio of two events falling into different magnitude ranges

16. Conclusion  This method is able to find precursor events just prior to an earthquake.  Unfortunately, it only works for short-term predictions and cannot predict years or months in advance.  Plenty of work can still be done!

17. References  “Predicting the Earthquake using Bagging Method in Data Mining”, S.Sathiyabama, K.Thyagarajah, D. Ayyamuthukumar  “A Bagging Method using Decision Trees in the Role of Base Classifiers”, Kristína Machová, František Barčák, Peter Bednár  “Cluster Analysis, Data-Mining, Multi-dimensional Visualization of Earthquakes over Space, Time and Feature Space”, Witold Dzwinel, David A. Yuen, Krzysztor Boryczko, Yehuda Ben-Zion, Shoichi Yoshioka, Takeo Ito  http://cse.stanford.edu/class/sophomore- college/projects-00/neural- networks/Architecture/feedforward.html