Amalgamation of Forecasting Environment Based upon Natural Disaster (Earth Quake) Analysis using DM Techniques

Abstract

Data mining is the computer supported process of determining and studying enormous sets of data and then take out the meaningful data. Data mining tools predicts performances and future trends, allowing businesses to make practical decisions. It can answer questions that conventionally were very time consuming to resolve. An event called forecast in a time series is more important for geophysics and economy difficulties. The time series data mining is an amalgamation (association) field of time series and data mining techniques. The ancient data are collected which has follow the time series organization, combine the data mining for pre-processing and finally apply the rules to predict the impact of earthquake which spoils the environment. Environment prediction has done by ancient earthquake time series to inspecting the method at first step ago. Enormous data sets are pre-processed using data mining techniques. Based on this process data prediction is possible. It focused on data mining techniques to analyze the environment through earthquake data. It describes data mining algorithms namely Random Forest, Classification Technique, Support Vector Machine (SVM) and Particle Swarm Optimization (PSO).