International Journal of Advanced Engineering Research and Science (IJAERS) [Vol-3, Issue-7, July- 2016] ISSN: 2349-6495(P) | 2456-1908(O) www.ijaers.com Page | 70 Survey on Analysis of Meteorological Condition Based on Data Mining Techniques M. Manikandan 1 , Dr. R. Mala 2 1 PG and Research Department of Computer Science, Marudupandiyar College of Arts and Science, Vallam, Thanjavur, Tamil Nadu 2 Research Advisor, Department of Computer Science, Marudupandiyar College of Arts and Science, Vallam, Thanjavur, Tamil Nadu Abstract— An application of data mining is a rich focus to Classification algorithm, Association algorithm, Clustering algorithm which can be applied to the field of various resources it concerns with developing methods that discover the knowledge from data origination. In this paper, focuses on meteorological data analysis in form of data mining is concerned to predict the knowledge of weather condition. Rainfall analysis, temperature analysis, based on climatic condition, cyclone form data analysis is vital application role for meteorological analysis in data mining techniques. Prediction, association and forecasting are the several method in data mining used for meteorological analysis. Many countries have already experienced deadly droughts and floods also climate-induced natural disasters have displaced hundreds of thousands of people across the world. Mainly due to over ambitious strategies and actions of human beings on the eco-system, data mining play a significant role in determining the climate trends in crucial manner. In this research work is discussing the application of different data mining techniques applied in several ways to predict or to associate or to classify or to cluster the pattern of meteorological data. It can be provided for future direction for research. Keywords— Meterological analysis, Correlation analysis, Forcasting, Clustering Techniques, Classification methods, Association Rule. I. INTRODUCTION In this paper represents the analyses of weather and climate events, comes into proper historical perspective, understanding their strangeness, and, increasingly, comparing recent events to expectations of future climate conditions. The climate conditions can monitoring yearly, seasonal, and monthly in regional and sectorial specific climate information, information on disasters and extreme events. Our earth is surrounded by a layer of air called atmosphere. Sometimes air becomes hot and sometimes it becomes cool this change in air is known as weather. The commonly seasons are winter, spring, summer, autumn. The most common data mining technique is used to identify and extract the weather condition based on Regression analysis, Correlation analysis, Artificial Neural Networks, Fuzzy Logic Techniques, Association rule, k-Nearest Neighbor and other classification techniques, multi linear regression analysis and clustering algorithm. In this paper to focuses an overview of data mining techniques used to analyzing the meteorological data for identifying the weather condition in terms of results show that given enough case data. II. RELATED WORKS Data mining techniques are now the important techniques utilized in all application area related to meteorological data for the prediction and decision making by discovering interesting rules or patterns or groups that indicate the relation between variables. The weather data used for the research include daily temperature, daily pressure and monthly rainfall. Akash D Dubey [20], presented different artificial neural networks have been created for the rainfall prediction of Pondicherry, a coastal region in India. These ANN [2] [14] models were created using three different training algorithms namely, feed-forward back propagation algorithm, layer recurrent algorithm and feed-forward distributed time delay algorithm. The number of neurons for all the models was kept at 20. The mean squared error was measured for each model and the best accuracy was obtained by feed-forward distributed time delay algorithm with MSE value as low as .0083. Pritpal Singh, et.al.[19], discussed about increased the accuracy of forecasting of Indian summer monsoon rainfall. In this study, they used feed-forward back- propagation neural network algorithm for ISMR forecasting. Based on this algorithm, five neural network architectures designated as BP1, BP2,…BP5 using three layers of neurons (one input layer, one hidden layer and one output layer). The data set is trained and tested separately for each of the neural network architecture, viz., BP1–BP5. The forecasted results obtained for the