s. atari

Airborne LiDAR (Light Detection and Ranging) has a high potential to provide 3D data for research and operational applications in a wide range of disciplines related to management of forest ecosystems and urban trees. Most proposed methods for extracting the individual trees first detect the points of tree top or bottom and then use them as starting points in a segmentation algorithm. Hence, in these methods, the number and the locations of detected peak points effect on the process of detecting individual trees heavily. In this study, a new method is presented to extract the individual tree segments using LiDAR points with 10 cm point density. In this method, a two-step strategy is performed for the extraction of individual tree LiDAR points: finding deterministic segments of individual trees points and allocation of other LiDAR points based on these segments. This research is performed on two study areas in Zeebrugge, Bruges, Belgium. The accuracy assessment of this method showed that with the increasing detection rate of young trees, it could correctly classified 74.51% of trees with 21.57% and 3.92% under- and over-segmentation errors, respectively.

The detailed and correct information on precipitation in different areas has an important role in the hydrological and climate studies of a region, such as the estimation of floodwaters, drought, runoff, sediment, river basin management, agriculture, irrigation scheduling and etc. Precipitation is a highly non-linear phenomenon, which changes temporally and spatially. Many factors influence precipitation variation. Generally, these factors can be divided into two climatic and geographical groups. Given technological developments, although it is not impossible to predict precipitation in the range of time and space, there are many complications. Despite many conceptual and statistical models that have been proposed to predict and forecast climatic variables, nowadays tools such as the artificial neural networks, decision trees and kernel-based methods are used to model hydrological processes and water engineering. In the current study, the efficiencies of support vector regression (SVR) and the Gaussian process regression (GPR) were investigated on prediction the amount of monthly precipitation in Mashhad. The sensitivity of precipitation to other meteorological parameters was also analyzed.
In this study, we use different kinds of meteorological parameters on monthly data scale in the Mashhad region, located in Razavi Khorasan Province in Iran. Different combinations of these meteorology parameters have been entered to support vector regression and Gaussian process regression as our chosen data mining methods. Support vector machines fall into two groups, including support vector regression and support vector classification. Based on the statistical learning theory, the support vector machine (SVM), introduced by Vapnik in 1995, is one of the supervised learning methods. Sometimes in this method, complicated and non-linear structures are required to separate data. The Gaussian process regression is a useful method employed to define prior distributions for the flexible models of regression and classification, in which regression or class probability functions are not limited to the simple parametric forms. The concept of Gaussian processes is based on the normal distribution, which was named after Carl Friedrich Gauss the Gaussian distribution. . It can be said that the Gaussian process is actually an infinite dimensional generalization for multivariate infinite distributions. The Gaussian processes are very important and prevalent in statistical modelling because they bear normal characteristics (Neal, 1997). Designing methods for support vector regression and Gaussian process regression also includes using the concept of the kernel function. In fact, with a non-linear transform from the input space to a characteristic space having more dimensions, even infinite, the problems can be made linearly separable. The most important kernel functions are linear, polynomial, normalized polynomial, radial basis function and Pearson function. In this study, kernel functions were used.
After investigating different kernel functions, it was observed that optimal results were obtained when the Pearson kernel function was employed in both support vector regression and Gaussian process regression. The research results indicated a higher accuracy and fewer errors when the parameters like monthly index, the mean of monthly relative humidity, the mean of maximum monthly relative humidity, difference between the means of minimum and maximum monthly temperatures and previous-month precipitation were used. This shows the greater impact of these parameters on precipitation. The results also indicated the higher efficiencies of modern data mining methods like support vector regression and the Gaussian process regression in predicting monthly precipitation. The Gaussian process regression provided the correlation coefficient, Nash-Sutcliffe coefficient, root-mean-square error, and the mean of absolute error with 0.870, 0.736, 12.37 (mm) and 7.85 (mm). It is introduced as the best method for predicting monthly precipitation in similar cases. The results also indicated that the Gaussian process regression was more powerful in predicting maximum monthly precipitation. It also led to more accurate predictions in cases that monthly precipitations were maximized, a fact which is very important and applicable in the prediction of floodwaters. Analyzing the sensitivities of models to input variables indicated that monthly precipitation was mostly influenced by previous-month precipitation, monthly index and the minimum monthly temperature. Both support vector regression and Gaussian process regression method had good performance in predicting monthly rainfall. The results showed that both methods had almost equal performance but that, in this case, Gaussian process regression provided more accurate predictions, especially in maximum precipitations. Therefore, this method could be considered an efficient and practical application for rainfall measurement.

In this research, the Asmari Formation΄s benthic foraminifera at Tang-e Shivi north-western flank of Nowdan Anticline in north of Kazerun (Interior Fars), with geographic coordinates E: 51° 44' 58'' and N: 29° 47' 42'', have been studied in order to introduce assemblage zones, age and paleoecology and correlate the age of the Asmari Formation with sections in Costal Fars and Izeh Zone (Firozabad, Dill, Shagabil). Its upper and lower boundaries are comfortable with Gachsaran and Pabdeh formations. Three assemblage zones at the studied section were recognized based on benthic foraminifera and biostratigraphy study. Detected biozones reflect Oligocene (Rupelian and Chattian) and Miocene (Aquitanian) age for this studied deposits with 348 meters thickness. Assemblage zone 1, with Firozabad section and assemblage zone 2 with 3 other sections and assemblage zone 3 with Dill anticline are correlatable.Environmental parameters such as: salinity, light, nutrients, temperature and depth, have played a role concerning the distribution of foraminifera. Accordingly, the Asmari Formation deposited in a normal salinity to hyper salinity water, aphotic to euphotic and oligophy to eutrophy zones. Grain supported microfacies (O4, O5, B, L1, L2, L4, L5 and L7), support reservoir quality for the Asmari Formation in subsurface areas, next to the studies section. The carbonate grain association types of the study section are nanofer, rodalgal, foralgal and foramol.

Markets are not limited to a specific geographic location. This leads to better decisions marketing by active economic indiviuals. World markets are valuable for guiding local markets. In this paper, the effect of the interaction among stock matkets of New York, London and Tokyo on Tehran stock market in January 15, 1999 to January 15, 2015 will be investigated. For the analysis, multivariable asymmetric variances model was used. The result showed that the effects of New York, Tokyo and London stock market on Tehran stock market were statistically significant. Also, all of the stock markets under study were affected by their lagged volatilities.

Gomizshan and Vazarshan are the names of two most important cycles of creation in Persian mythology during which the battle between two forces of goodness and vice reaches its most severe stage and at last leads to the final victory of the good over the vicious. Belief in the struggle between good and vicious principles which constitutes the basis of Persian mythical thought is manifested in Shahnameh in the form of long battles and wars between Iran and Aniran (non-Iranian). In Shahnameh، Iran symbolizes Ahuramazda whereas Aniran (mostly Turan) mostly symbolizes the land of Ahriman and the struggle between the two lands embodies Gomizshan in Persian myths which is finished by Keikhosrow at the end of epical part of Shahnameh and reaches its Vazarshan in Shahnameh. Based on psychological views of Carl Gustav Jung، this article aims to explore the roots of dualism in Persian mythical thought and thus Ferdowsi’s Shahnameh، and good and vicious aspects are decoded as the symbols of consciousness and unconsciousness in such kind of psychology. The results of this study show that، in the dualist attitude of the myth، the worlds of goodness and badness are comparable with consciousness and unconsciousness respectively، and the opposition Ahuramazda/Ahriman or Iran/Aniran in Shanhnameh and mythical thought can be evaluated as the struggle between consciousness and unconsciousness for reaching the perfect Self. Since the sequence of ups and downs of prominent kings in Shahnameh ends with the final victory of Iran over Turan in the reign of Keikhosrow، it can be said that the spirit of national epic which had been blown into the body of Kiomars from the beginning leads to the immortality of Keikhosrow via passing different embodiments، which can be the symbols of persona in Jung theory، with continuous sequence of life and death archetype.