AbstractsGeography &GIS

Application of data mining techniques for Novel ensemble forecast of lead seven Days minimum and maximum surface air Temperature in Chennai india;

by Ramesh K




Institution: Anna University
Department: Application of data mining techniques for Novel ensemble forecast of lead seven Days minimum and maximum surface air Temperature in Chennai india
Year: 2015
Keywords: Predictive data mining; Statistical computational and machine learning tools
Record ID: 1183942
Full text PDF: http://shodhganga.inflibnet.ac.in/handle/10603/38555


Abstract

Evolution in data storage and large databases has generated an newlineimperative need for new techniques and tools for data analysis and knowledge newlinediscovery Statistical computational and machine learning tools have been newlineused by scientific community and researchers in the area of scientific data newlineanalysis Data mining is a data analysis technique which explores data and newlinediscovers meaningful information and knowledge Predictive data mining will newlinebe a good aid for weather forecasting which predicts the future state of the newlineatmosphere with the present state information newlineAmong weather elements surface air temperature is the key newlinedeterminant for vegetation animals and human livelihood in a particular newlinelocation of earth which also influences most atmospheric parameters like newlineprecipitation humidity pressure wind speed and wind direction Since newlinedeviations in surface air temperature claims many lives in the earth timely newlineprediction of minimum and maximum surface air temperature will help in newlineplanning and governing very hot and very cold climate However it is newlinechallenging because of the dynamic atmospheric parameters associated with newlinethe temperature event newlineIn the proposed work data mining technique based ensemble newlineforecast of lead seven days minimum and maximum surface air temperature is newlinedeveloped for the location Chennai India newline newline%%%appendix p161-163, reference p164-174.