Study on Techniques of Earthquake Prediction

Abstract

An event called prediction in a time series is more important for geophysics and economy problems. The time series data mining is a combination field of time series and data mining techniques. The historical data are collected which has follow the time series methodology, combine the data mining for preprocessing and finally apply the fuzzy logic rules to predict the impact of earthquake. Earthquake prediction has done by historical earthquake time series to investigating the method at first step ago. Huge data sets are preprocessed using data mining techniques. Based on this process data prediction is possible. This paper is focused on statistics and soft computing techniques to analyze the earthquake data. 1. The Seasonal component 2. The Trend component and 3. The Irregular component The seasonal data are systematic or regular movements of data. The Trend data mean by Long-term fluctuations and the irregular data mean by unsystematic or short-term fluctuations. The major utility area of the time series model is statistical forecasting. The available prediction approaches are regression, time series and chaotic approaches. Each and every method has its own advantage and disadvantage. The historical sequences of data are to be used for forecasting purposes, because of this reason the time series models are used to predict the future values. The prediction will show what will happen but won't why it happens. Time series values are transformed to phase space by using a nonlinear method and then apply the fuzzy logic to predict optimum value. The time series data are derived from the time interval of any system. Traditional stationary time series models are Autoregressive Integrated Moving Average (ARIMA) and Minimum Mean Square Error forecasting methods. Data mining is used to extract useful and more relevant information from the huge database. The author Han describes an artificial intelligence and pattern recognition methods for prediction (7). The time series data mining is used for prediction of earthquake (5). Fuzzy logic methods are used to predict the earthquake, stock market changes, weather forecasting and gold price changes. The similarities of patterns are selected as a fuzzy set; these sets are specified in membership function. The fuzzy logic is accurately predicted the prediction event. The concepts of time series data mining had been used for clustering and natural event prediction. Povinelli, (8) suggested an application for the event prediction. In their study, they focus the different applications such as earthquake prediction, fall of stock price and gold price prediction. The major advantage of time series is possible to predict the future value based on the previous historical data. The study of the past sequence of historical data may be more valuable. The time series is helpful to predict the next sequence of future values. The utility of time series method is specifically for Trend analysis, Trade market, Finance, Climatologic and earthquake prediction.