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Predicting Earthquakes

Predicting Earthquakes. By Lois Desplat. 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. Estimating earthquake probabilities.

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Predicting Earthquakes

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  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 • Finite automata • Supervised learning techniques: • Decision Tree • Bayesian Classification • Feed-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 noisei.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

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

  12. Visualization of the Data Space

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

  14. 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!

  15. 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

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