Earthquake Prediction using Data Mining Tools

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• Earthquake Prediction using Data Mining Tools
• Mrinalini Kabbur
• Ritu Chinya
• Progress Report

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Project

• Earthquake classification and prediction using
  Data mining tools
• Weka will be used to develop the model
• Naïve Bayesian is used to classify unknown class
  label.
• Ten-Fold Cross validation to estimate the
  accuracy.

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Tasks Completed So far

• Install Weka
• Weka is a set of software for machine learning
  and mining
• Developed at the University of Waikato in New
  Zealand
• Available for free
• Easy to use Graphical User Interface

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• Learn Weka
• Both of us were new to weka
• Used tutorial by Svetlana Aksanova
• Looked up the internet for additional information
  on using Weka
• EarthQuake Data Set
• Consists of the Earthquakes that happened in
  the Northern California region during 2005.
• Data gathered from United States Geological
  Survey (USGS) website.

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• Attributes
• Longitude
• Latitude
• Depth
• Class Label
• Magnitude
• Data preprocessing
• Current Data in Excel format
• Weka algorithms work on ARFF format
• Conversion from Excel to ARFF format
• Save the Excel file as csv

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Work do be Done.

• Descritize
• Attributes contain numeric data.
• Some Weka algorithms like ID3 require nominal
  attribute Values.
• Convert numeric attributes to mominal
• Apply Classification rules to come up with
• Decision trees
• Rules sets

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• Algorithms used for modelling
• ID3
• C4.5
• Naïve Bayesian
• Accuracy Estimation
• Ten fold Cross validation
• Write Project Report

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Schedule

• Complete all work by May 1st.
• Share the work equally between the two

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Reference

• http//www.studentprogress.com/appln/colleges/cogr
  ec/Papers/D_05.pdf
• www.meteoquake.org/our.html
• http//www.cs.waikato.ac.nz/ml/weka/index.html
• http//gaia.ecs.csus.edu/mei/215/tutorial.html
• http//www.ngdc.noaa.gov/seg/hazard/sig_srch_idb.s
  html
• Weka Explorer tutorial by Svetlana Aksanova