Site selection for a radio astronomy observatory in Turkey: atmospherical, meteorological, and radio frequency analyses

Selecting the future site for a large Turkish radio telescope is a key issue. The National Radio Astronomy Observatory is now in the stage of construction at a site near Karaman City, in Turkey. A single-dish parabolic radio antenna of 30?C40 m will be installed near a building that will contain offices, laboratories, and living accommodations. After a systematic survey of atmospheric, meteorological, and radio frequency interference (RFI) analyses, site selection studies were performed in a predetermined location in Turkey during 2007 and 2008. In this paper, we described the experimental procedure and the RFI measurements on our potential candidate??s sites in Turkey, covering the frequency band from 1 to 40 GHz.     

Mapping and assessment-based modeling of soil fertility differences in the central and eastern parts of the Black Sea region using GIS and geostatistical approaches

This study was carried out on arable lands of the central and eastern Black Sea regions including eight provinces (Artvin, Giresun, Gümü?hane, Ordu, Rize, Samsun, Sinop, and Trabzon). The present study aims to generate a soil fertility map for agricultural lands in the central and eastern parts of the Black Sea region. The main objective of this research is to quantify soil fertility by developing a soil fertility index (SFI) model at the regional level. The related objectives were to map the spatial distribution of soil fertility by using auxiliary variables and to model soil fertility within the study region. To accomplish this, a data set for soil fertility differences was collected and a model was developed to predict the spatial distribution of differences across the region. The study area was divided into 2.5 × 2.5-km grid squares. A total of 3400 soil samples were collected from the surface (0–20 cm) of each grid intersection point. The geostatistical method was used to generate the SFI distribution map of the study area for surface soils. Of the total study area, 93.76% had good (S1) or moderately fertile (S2) soil while 6.15% of the area had marginally fertile (S3) soil. Only a very small area (N) had low-fertility soil.     

Wavelet–copula‐based mutual information for rainfall forecasting applications

Under a climate change, the physical factors that influence the rainfall regime are diverse and difficult to predict. The selection of skilful inputs for rainfall forecasting models is, therefore, more challenging. This paper combines wavelet transform and Frank copula function in a mutual information‐based input variable selection (IVS) for non‐linear rainfall forecasting models. The marginal probability density functions (PDFs) of a set of potential rainfall predictors and the rainfall series (predictand) were computed using a wavelet density estimator. The Frank copula function was applied to compute the joint PDF of the predictors and the predictand from their marginal PDFs. The relationship between the rainfall series and the potential predictors was assessed based on the mutual information computed from their marginal and joint PDFs. Finally, the minimum redundancy maximum relevance was used as an IVS stopping criterion to determine the number of skilful input variables. The proposed approach was applied to four stations of the Nigerien Sahel with rainfall series spanning the period 1950–2016 by considering 24 climate indices as potential predictors. Adaptive neuro‐fuzzy inference system, artificial neural networks, and random forest‐based forecast models were used to assess the skill of the proposed IVS method. The three forecasting models yielded satisfactory results, exhibiting a coefficient of determination between 0.52 and 0.69 and a mean absolute percentage error varying from 13.6% to 21%. The adaptive neuro‐fuzzy inference system performed better than the other models at all the stations. A comparison made with KDE‐based mutual information showed the advantage of the proposed wavelet–copula approach.     

Local indications of climate changes in Turkey: Bursa as a case example

This study aims to put out on what ratio Bursa province, one of the important heavy industry regions of Turkey, has been affected climatic process called “Global Warming” or “Climate Change”. For this intend climatic measurement results from Bursa center, top of Uludağ Mount, Yenişehir and Keles meteorological stations were used. These measurements were taken as minimum temperature at night-time, maximum temperature at day-time, and mean temperature, mean pressure, insolation intensity, insolation duration, mean wind speed, minimum temperature above soil, soil temperatures at depths of 5, 10, and 20 cm rainfall. Overall, our statistical results showed that there was a considerable warming at statistically 1% and 5% levels in summer months, particularly in July Almost all performed measurements confirm this result. According to climatic data for thirty years (1975–2005), in the last twelve years contrary to previous 18 years, mean temperature values were higher than long-term mean value nine times (years) repetitively. Temperatures did not deviated higher than 0.5°C in six of these. At the temperatures below mean, The maximum deviation was −0.4°C.     

Seismic landslide analysis: Gurpinar (Istanbul) as a case history

Slope failures triggered by earthquakes are among the most important soil mechanics problems. In this study, static and pseudostatic analyses of slope stability for earthquake conditions were carried out in the Gurpinar area. In situ testing (SPT) was carried out and laboratory samples were obtained from six boreholes (maximum depth 50.0 m) to determine soil classification and strength characteristics. Geophysical studies (seismic refraction and MASW) were also carried out in the area to estimate the structure and stiffness strength characteristics of the slope to 50.0 m depth. All field and laboratory data were used to determine the mechanical and structural (geometrical) behavior of the slope. In order to solve the slope stability problem, three soil slope models were considered. Pseudostatic analysis was carried out to estimate the earthquake acceleration seismic hazard in the region. These analyses showed that, while there was no potential slope instability under static load conditions, some problems would appear with increasing earthquake acceleration. A geotechnical slope improvement project is proposed for the study area.     

Automatic building extraction from very high-resolution image and LiDAR data with SVM algorithm

Recent developments in the field of remote sensing have introduced new sensor technologies in usage of LiDAR, SAR, and high-resolution optical data. Classification performance is expected to increase through combining these various data sources. The purpose of this study is to develop a new approach for automatic extraction of buildings in urbanized and suburbanized areas. For this purpose, multi-feature extraction process including the spatial, spectral, and textural features were conducted on the very high spatial resolution multispectral aerial images and the LiDAR data set. SVM algorithm was trained by using this multi-feature data, and the classification was performed. After the classification of building and non-building, objects were extracted with high accuracy for the test areas. As a result, it has been proven that multi-features derived from combination of optical and LiDAR data can be successfully applied to solve the problem of automatic detection of buildings by using the proposed approach.     

Numerical analysis under seismic loads of soils improvement with floating stone columns

Geotechnical Engineering has developed many methods for soil improvement so far. One of these methods is the stone column method. The structure of a stone column generally refers to partial change of suitable subsurface ground through a vertical column, poor stone layers which are completely pressed. In general terms, to improve bearing capacity of problematic soft and loose soil is implemented for the resolution of many problems such as consolidation and grounding problems, to ensure filling and splitting slope stability and liquefaction that results from a dynamic load such as earthquake. In this study, stone columns method is preferred as an improvement method, and especially load transfer mechanisms and bearing capacity of floating stone column are focused. The soil model, 32 m in width and 8 m in depth, used in this study is made through Plaxis 2D finite element program. The clay having 5° internal friction angle with different cohesion coefficients (c 10, c 15, c 20 kN/m²) are used in models. In addition, stone columns used for soil improvement are modeled at different internal friction angles (? 35°, ? 40°, ? 45°) and in different s/D ranges (s/D 2, s/D 3), stone column depths (B, 2B, 3B) and diameters (D 600 mm, D 800 mm, D 1000 mm). In the study, maximum acceleration (a _max = 1.785 m/s²) was used in order to determine the seismic coefficient used. In these soil models, as maximum acceleration, maximum east–west directional acceleration value of Van Muradiye earthquake that took place in October 23, 2011 was used. As a result, it was determined that the stone column increased the bearing capacity of the soil. In addition, it is observed that the bearing capacity of soft clay soil which has been improved through stone column with both static and earthquake load effect increases as a result of increase in the diameter and depth of the stone column and decreases as a result of the increase in the ranges of stone column. In the conducted study, the bearing capacity of the soil models, which were improved with stone column without earthquake force effect, was calculated as 1.01–3.5 times more on the average, compared to the bearing capacity of the soil models without stone column. On the other hand, the bearing capacity of the soil models with stone columns, which are under the effect of earthquake force, was calculated as 1.02–3.7 times more compared to the bearing capacity of the soil models without stone column.     

Convergence of per capita carbon dioxide emissions: implications and meta-analysis

There is a rich empirical literature testing whether per capita carbon dioxide emissions tend to converge over time and across countries. This article provides a meta-analysis of the results from this research, and discusses how carbon emissions convergence may be understood in, for instance, the presence of international knowledge spillovers and policy convergence. The results display evidence of either divergence or persistent gaps at the global level, but convergence of per capita carbon dioxide emissions between richer industrialized countries. However, the results appear sensitive to the choice of data sample and choice of convergence concept, e.g. stochastic convergence versus β-convergence. Moreover, peer-reviewed studies have a higher likelihood of reporting convergence in carbon dioxide emissions compared to non-refereed work.

POLICY RELEVANCE

The empirical basis for an egalitarian rule of equal emissions per capita in the design of global climate agreements is not solid; this supports the need to move beyond single allocation rules, and increase knowledge about the impacts of combined scenarios. However, even in the context of the 2015 Paris Agreement with its emphasis on voluntary contributions and ‘national circumstances’, different equity-based principles could serve as useful points of reference for how the remaining carbon budget should be allocated.