Of the natural hazards in Turkey, landslides are the second most devastating in terms of socioeconomic losses, with the majority of landslides occurring in the Eastern Black Sea Region. The aim of this study is to use a statistical approach to carry out a landslide susceptibility assessment in one area at great risk from landslides: the Sera River Basin located in the Eastern Black Sea Region. This paper applies a multivariate statistical approach in the form of a logistics regression model to explore the probability distribution of future landslides in the region. The model attempts to find the best fitting function to describe the relationship between the dependent variable, here the presence or absence of landslides in a region and a set of independent parameters contributing to the occurrence of landslides. The dependent variable (0 for the absence of landslides and 1 for the presence of landslides) was generated using landslide data retrieved from an existing database and expert opinion. The database has information on a few landslides in the region, but is not extensive or complete, and thus unlike those normally used for research. Slope, angle, relief, the natural drainage network (including distance to rivers and the watershed index) and lithology were used as independent parameters in this study. The effect of each parameter was assessed using the corresponding coefficient in the logistic regression function. The results showed that the natural drainage network plays a significant role in determining landslide occurrence and distribution. Landslide susceptibility was evaluated using a predicted map of probability. Zones with high and medium susceptibility to landslides make up 38.8 % of the study area and are located mostly south of the Sera River Basin and along streams.

In this study, data from two different meteorology stations were analyzed in order to reveal the effects of the urbanization on the soil temperature. These stations are the Ankara Meteorology Station (AMS), showing the urban effects, and the Esenboğa Meteorology Station (EMS), showing the rural effects. The soil temperatures measured at depths of 5, 10, 20, and 50 cm at 0700, 1400, and 2100 hours between 1960 and 2005 were used in the analysis. Long-term mean monthly temperatures at each depth and at each time considered were calculated and analyzed using Sen's slope and Mann-Kendall tests. The results showed that the mean monthly urban soil temperatures were generally higher than the rural soil temperatures. The differences between temperatures measured at 5, 10, 20, and 50 cm in urban and rural stations (DeltaT(s(AMS-EMS))) ranged between 1.8 degrees C and 2.1 degrees C. As in the urban heat islands, the differences between the urban and rural soil temperatures are high at 2100 hours and low at 1400 hours. It was also observed that, due to the increasing number of buildings around the Esenboğa Station in recent years, the difference between the urban and rural soil temperatures seems to have become smaller. These show that the factors affecting the urban heat islands and those affecting the soil temperatures are similar. Also, the temperature differences were observed to be higher during the warm season than in the cold season. The frequency distributions of the temperature differences (DeltaT(s(AMS-EMS))) reveal both positive and negative values. However, the positive temperature differences are obviously prevalent.

The article “Landslide susceptibility mapping of the Sera River Basin using logistic regression model,” written by Nussaïbah B. Raja, Ihsan Çiçek, Necla Türkoğlu, Olgu Aydin, and Akiyuki Kawasaki, was originally published Online First without open access.

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