The estimation of climate parameters using data mining techniques

Nowadays, estimation of climate parameters is a crucial phenomenon. The rationale behind this phenomenon is that important decisions in many applications are based on predicting the weather. Data mining is widely used by enterprises to discover knowledge from large databases and data mining techniques are essential for estimation of climate parameters. The data of these parameters are huge and need prediction mechanisms. In this thesis, a methodology for wind velocity prediction is proposed. Predictive data mining algorithms neural networks, linear regression and Support Vector Machine (SVM), are used to estimate wind velocity. A prototype application is built to demonstrate proof of the concept. The prototype exploits Weka data mining API provided using Java programming language. The climate dataset used in the experiments has a large amount of data from intelligent stations, for every day of the month from several different regions. It has attributes such as station number, month, day, pressure, humidity and temperature. Wind velocity is the parameter for which prediction is made by using the three different algorithms. Wind velocity estimation has its utility in weather forecasting. The relation between velocity and weather forecasting is the start point of the thesis. Based on the existing data available on climate parameters, the chosen data mining algorithms perform their logic in order to estimate the wind velocity. After completing computations, the observations are presented and the results are compared with actual wind velocity. The error rate is also considered to evaluate the performance of the three algorithms. From the empirical study, it is understood that the prediction performance of Linear Regression is higher than the other two data mining algorithms.