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- W2566496798 abstract "A natural disaster is the effect of a natural hazard (e.g., flood, tornado, hurricane, volcanic eruption, earthquake, heatwave, or landslide). Earthquakes, landslides, tsunamis and volcanos are complex physical phenomenon that leads to financial, environmental or human losses. Prediction of such geological disasters is the need of the day. Also, prediction of these disasters is a complex process that depends on many physical and environmental parameters. Many approaches exist in the literature based on scientific and statistical analysis. Data mining techniques can also be used for prediction of these natural hazards. This paper presents a review of application of data mining in the prediction of natural geological calamities. 16 journal articles on the subject published between 1989 and 2011 was analyzed. The main data mining techniques used for earthquake prediction are logistic models, neural networks, the Bayesian belief network, and decision trees, all of which provide primary solutions to the problems inherent in the prediction of earthquakes, tsunamis, landslides and other micro seismic activities. This paper also aims to encourage additional research on topics, and concludes with several suggestions for further research." @default.
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- W2566496798 date "2012-01-01" @default.
- W2566496798 modified "2023-09-24" @default.
- W2566496798 title "A Review of Application of Data Mining in Earthquake Prediction" @default.
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