The effect of the relative humidity and the specific humidity on the determination of the climate regions in Turkey

All elements of climate that affect climatic events must be taken into account such that the climate regions are determined with exactitude. To this end, data on maximum temperature (Tx), minimum temperature (Tn), mean temperature (Tm), and precipitation (Pt) as well as local pressure (Ps), mean wind (WN), relative humidity (RH), and specific humidity (SH) have been investigated statistically and graphically. The specific humidity data calculated using Tm, Ps, and RH data and statistical comparisons have shown that there are no drawbacks to using SH in climatologic studies. According to principal component analysis, it was concluded that RH and SH should be used together with Tx, Tm, Tn, and Pt for the determination of the climate regions. Two cluster analysis methods, Ward's method and Kohonen neural network technique, were used to show the effect of RH and SH. A comparison of the cluster's stability between the limited and high number of stations shows that Ward's method and Kohonen neural network are very stable in both cases. It was also determined that RH does not change the outline of climate regions but that it affects the zones of climate transition. It was observed that clusters determined by using Tm, Pt, and RH provide relatively more distinctive clusters in the data space than clusters determined by using Tm, Pt, and SH.     

Flow around circular and noncircular cylinders by a low-order panel method

Time-dependent cross-flow was studied around cylinders with circular and noncircular cross-sections. The numerical approach for the analysis was a low-order panel method based on constant source and dipole values along each panel. The method was previously used successfully for several applications, such as calculation of the added mass and damping coefficients. In simulating the viscous time-dependent flow around the cylinder, the time-dependent wake feature of the code was used. For the circular and D-cylinders, the results agreed well with the experiments. Suggestions for improving the results for T-cylinders with angle of attack are included.     

Landslide susceptibility mapping using GIS-based multi-criteria decision analysis,support vector machines,and logistic regression

Identification of landslides and production of landslide susceptibility maps are crucial steps that can help planners, local administrations, and decision makers in disaster planning. Accuracy of the landslide susceptibility maps is important for reducing the losses of life and property. Models used for landslide susceptibility mapping require a combination of various factors describing features of the terrain and meteorological conditions. Many algorithms have been developed and applied in the literature to increase the accuracy of landslide susceptibility maps. In recent years, geographic information system-based multi-criteria decision analyses (MCDA) and support vector regression (SVR) have been successfully applied in the production of landslide susceptibility maps. In this study, the MCDA and SVR methods were employed to assess the shallow landslide susceptibility of Trabzon province (NE Turkey) using lithology, slope, land cover, aspect, topographic wetness index, drainage density, slope length, elevation, and distance to road as input data. Performances of the methods were compared with that of widely used logistic regression model using ROC and success rate curves. Results showed that the MCDA and SVR outperformed the conventional logistic regression method in the mapping of shallow landslides. Therefore, multi-criteria decision method and support vector regression were employed to determine potential landslide zones in the study area.     

Comparative analysis of regression and artificial neural network models for wind speed prediction

In this study, wind speed was modeled by linear regression (LR), nonlinear regression (NLR) and artificial neural network (ANN) methods. A three-layer feedforward artificial neural network structure was constructed and a backpropagation algorithm was used for the training of ANNs. To get a successful simulation, firstly, the correlation coefficients between all of the meteorological variables (wind speed, ambient temperature, atmospheric pressure, relative humidity and rainfall) were calculated taking two variables in turn for each calculation. All independent variables were added to the simple regression model. Then, the method of stepwise multiple regression was applied for the selection of the “best” regression equation (model). Thus, the best independent variables were selected for the LR and NLR models and also used in the input layer of the ANN. The results obtained by all methods were compared to each other. Finally, the ANN method was found to provide better performance than the LR and NLR methods.     

Variance component estimation applied to satellite laser ranging

The variance component estimation (VCE) method as developed by Helmert has been applied to the global SLR data set for the year 1987. In the first part of this study the observations have been divided into two groups: those from ruby and YAG laser systems, and their weights estimated over several months. It was found that the weights of both sets of stations altered slightly from month to month, but that, not surprisingly, the YAG systems consistently outperformed those based on ruby lasers. The major part of this paper then considers the estimation of the variance components (i.e. weights) at each SLR station from month to month. These were tested using the F-statistic and, although it indicated that most stations had significant temporal variations, they were generally small compared with the differences between the stations themselves, i.e. the method has been shown to be capable of discriminating between the precision with which the various laser stations are operating. The station coordinates and baseline lengths computed using both a priori, and estimated, weights where also compared and this showed that changes in the weights can have significant effects on the estimation of the station positions, particularly in the z component, and on the baseline lengths - so proving the importance of proper stochastic modelling when processing SLR data.     

Modeling of Trophospheric Ozone Concentrations Using Genetically Trained Multi-Level Cellular Neural Networks

Tropospheric ozone concentrations, which are an important air pollutant, are modeled by the use of an artificial intelligence structure. Data obtained from air pollution measurement stations in the city of Istanbul are utilized in constituting the model. A supervised algorithm for the evaluation of ozone concentration using a genetically trained multi-level cellular neural network （ML-CNN） is introduced, developed, and applied to real data. A genetic algorithm is used in the optimization of CNN templates. The model results and the actual measurement results are compared and statistically evaluated. It is observed that seasonal changes in ozone concentrations are reflected effectively by the concentrations estimated by the multilevel-CNN model structure, with a correlation value of 0.57 ascertained between actual and model results. It is shown that the multilevel-CNN modeling technique is as satisfactory as other modeling techniques in associating the data in a complex medium in air pollution applications.     

Simulation of three-dimensional finite-depth wave phenomenon for moving pressure distributions

The flow field and the bottom pressure signatures due to an air cushion vehicle are calculated by analytical and computational means. The singularities in the integrals from the theoretical analyses are removed by using the Cauchy's residue theorem and the resulting integrals are numerically evaluated by the adaptive quadrature routines of QUADPACK.