In this study, a new per-field classification method is proposed for supervised classification of remotely sensed multispectral image data of an agricultural area using Gaussian mixture discriminant analysis (MDA). For the proposed per-field classification method, multivariate Gaussian mixture models constructed for control and test fields can have fixed or different number of components and each component can have different or common covariance matrix structure. The discrimination function and the decision rule of this method are established according to the average Bhattacharyya distance and the minimum values of the average Bhattacharyya distances, respectively. The proposed per-field classification method is analyzed for different structures of a covariance matrix with fixed and different number of components. Also, we classify the remotely sensed multispectral image data using the per-pixel classification method based on Gaussian MDA.
Risk analyses made in the area of seismic activity are going to be of great importance in determining the earthquake interoccurence times. Several statistical methods have been developed for this purpose. Recently, Exponential, Gamma and Weibull distributions are the frequently used methods in this regard. In this study, we investigate the interoccurence time statistics of earthquakes which occurred in the area coordinated 39º–42º N latitude and 30º–40º E longitude in the North Anatolian Fault Zone (NAFZ) between the years 1960–2008, with a mixture of two different distributions of Exponential, Gamma and Weibull and a mixture of the same kind of distribution. We found that the mixture distributions are more suitable than the other examined distribution models for small magnitudes (mc ≥ 3). Also Weibull-Gamma and Weibull-Exponential distributions are agreeable for large magnitudes (mc ≥ 5).
Abstract-The world's main energy source is the sun. Other energy sources are caused directly or indirectly from the sun. Turkey has a rich potential in terms of solar energy and interest in solar power systems is increasing in the rapidly evolving technology. In all of the solar energy studies needs solar radiation data but solar radiation measurements are not possible on each area. Therefore, estimation of the solar radiation by using a variety of methods is emerging importance. In this study, Turkey and Adiyaman solar energy potential is investigated and statistical analysis was performed for Adiyaman. Various parameters were estimated by using actual solar radiation data from the Meteorology and experimental data were compared with theoretical ones. According to the results of the statistical analyzes, Adiyaman data indicate the best fit with the cubic model.
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