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Predicting Earthquakes

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Title: Predicting Earthquakes


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 gt 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
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13
Visualization of the Data Space
14
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15
  • 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

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 Barcák, 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/pr
    ojects-00/neural-networks/Architecture/feedforward
    .html
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