Early detection of hazardous weather conditions in Turkey with satellite images using Support Vector Machines and Artificial Neural Networks

The prediction of meteorological phenomena resulting from rainfall, is one of the most important elements of the minimization of the damages. Local meteorological radars works at regional base hence they cannot represent the parameters of precipitation. Because of insufficiency of the radar systems early detection, satellite images can be used to create decision systems. At this point firstly infrared satellite images will be classified and then comparative results will be discussed on experimental stage. In this work, we used Wavelet Transform applied to infrared satellite images to extract approximation coefficients. Size of these coefficients are reduced using Principal Component Analysis and classified through classification algorithms. Artificial Neural Network and Support Vector Machines are used for classification. As a result of the classification made with Artificial Neural Networks, we accomplished 84% prediction rate. With the classification of Support Vector Machines, we reached 93% prediction rate.