Murat Ercanoglu

Discontinuity controlled probabilistic slope failure risk maps of the Altindag (settlement) region in Turkey

The evaluation of potential rock slope problems using stereographic projection techniques known as kinematic analysis is one of the most important parts of a slope stability investigation to be carried out in jointed rock media. In conventional stereoprojection techniques for the assessment of possible rock slope failures, the peak orientations of joints together with the slope geometry and the friction angle of the weakness planes are used. Other possible joint orientations which may be encountered in the rock media are ignored, although they belong to the group of joint peak orientations. In this study, nearly vertical jointed andesites cropped out at the Altindag settlement region in Ankara were studied in order to evaluate the relevance of this ignored discontinuity orientation data on slope stability. As a result, probabilistic risk maps for planar, toppling and wedge failures were produced using the kinematic rules and digital elevation model of the study area. The comparison of the distribution of the actual failures in the area and the probabilistic risk maps prepared for the study area revealed that all of the identified failures are found to be located in the higher risk zones on the probabilistic risk maps.

Estimating the deformation modulus of rock masses: a comparative study

Although the modulus of deformation of rock masses has crucial importance for geotechnical projects, such as tunnels and dams, the determination of this parameter by in situ tests requires considerable costs and involves difficult operational processes. For this reason, empirical equations for the indirect estimation of the modulus of deformation are an interesting issue for rock engineers and engineering geologists. This study includes assessment of the prediction performances of some existing empirical equations, using in situ plate loading test data and rock mass properties, producing an empirical equation depending on the new data, construction of a fuzzy inference system for the estimation of modulus of deformation, and making a comparison between results obtained from the empirical equations and fuzzy inference system. A series of calculations and statistical analyses were undertaken. It is concluded that the performance of the empirical equations and fuzzy inference system obtained in this study is satisfactory. However, the prediction models developed in this study are limited by the number of the data used and the rock types employed. For these reasons, a cross-check should be performed before using these prediction models for design purposes.

Dynamics of a complex mass movement triggered by heavy rainfall: a case study from NW Turkey

Abstract Following a period of heavy precipitation, a large and complex mass movement, namely the Dagkoy landslide, occurred in the West Black Sea Region of Turkey on May 21, 1998. This paper describes the conditioning factors of the landslide and interprets the mass transport processes in terms of a movement scenario. Geology, geomorphology and vegetation cover were considered as the conditioning factors of the failure. Observations showed that the gently sloping (about 10°) area is mostly covered by dense forest trees at the crown where the motion initiated. Significant intersection of the collapsed slope with dip of the local marls seems to have contributed to the formation and geometry of the landslide. The distance from the crown down to the toe of the landslide measured more than 600 m, with about 0.6 km 3 total earth material displaced. The landslide has both a block sliding characteristics in the upper portions and a debris flow/soil flow component around the margins of the sliding blocks in the middle parts and at the toe. The proposed scenario for the landslide reveals that the movement was initiated near crown as a result of the excess water content in the marls at the end of 3 days of heavy rainfall. The early perturbations (transverse cracks, ridges, etc.) lasted for 6–7 h, after which the central part of the zone started to move as a soil flow in which very large intact blocks were transported. Even though the movement was very rapid (1.2 m/min), there was no loss of life. However, the movement destroyed 38 houses, one mosque and a considerable amount of farmland.

Modeling Wildland Fire Susceptibility Using Fuzzy Systems

Due to fire suppression efforts, many areas have developed conditions whereby fire susceptibility is high. To help identify those areas and improve fire management, two fire susceptibility models were developed for a study area in southeastern Idaho. Both models used the same intrinsic parameters (topography, fuel characteristics, etc). The difference between the models is the first used expert knowledge to weight input parameters, whereas the second relied upon fuzzy systems to derive the weighting. Comparing the resulting output models indicates that the first more accurately capture fire susceptibility. This lends credibility to the use of expert knowledge in geo-spatial modeling.

Application of Chebyshev theorem to data preparation in landslide susceptibility mapping studies: an example from Yenice (Karabük, Turkey) region

Landslide database construction is one of the most crucial stages of the landslide susceptibility mapping studies. Although there are many techniques for preparing landslide database in the literature, representative data selection from huge data sets is a challenging, and, to some extent, a subjective task. Thus, in order to produce reliable landslide susceptibility maps, data-driven, objective and representative database construction is a very important stage for these maps. This study mainly focuses on a landslide database construction task. In this study, it was aimed at building a representative landslide database extraction approach by using Chebyshev theorem to evaluate landslide susceptibility in a landslide prone area in the Western Black Sea region of Turkey. The study area was divided into two different parts such as training (Basin 1) and testing areas (Basin 2). A total of nine parameters such as topographical elevation, slope, aspect, planar and profile curvatures, stream power index, distance to drainage, normalized difference vegetation index and topographical wetness index were used in the study. Next, frequency distributions of the considered parameters in both landslide and nonlandslide areas were extracted using different sampling strategies, and a total of nine different landslide databases were obtained. Of these, eight databases were gathered by the methodology proposed by this study based on different standard deviations and algebraic multiplication of raster parameter maps. To evaluate landslide susceptibility, Artificial Neural Network method was used in the study area considering the different landslide and nonlandslide data. Finally, to assess the performances of the so-produced landslide susceptibility maps based on nine data sets, Area Under Curve (AUC) approach was implemented both in Basin 1 and Basin 2. The best performances (the greatest AUC values) were gathered by the landslide susceptibility map produced by two standard deviation database extracted by the Chebyshev theorem, as 0.873 and 0.761, respectively. Results revealed that the methodology proposed by this study is a powerful and objective approach in landslide susceptibility mapping.

Determination of Coal Rank by Fuzzy Logic: A Case Study of Coal from Central Anatolia, Turkey

Koyunagili coalfield in the Beypazari basin of the Central Anatolia was investigated and coal ranks were determined using fuzzy Mamdani model based on ASTM coal rank classification. This is a pilot study to investigate the applicability of the fuzzy model to determine the ranking of any coal seam. The Koyunagili coalfield, which is located at the southern part of the Beypazari basin, consists of the late Miocene coal seam within the tuffaceous rocks. The mineable coal seam is split by a 0.5–0.55 m thick clayey limestone into the upper (1.2–1.3 m thick) and lower (0.4 m thick) benches. A total of 28 profile coal samples from underground mines were collected. The samples have an average of 30.9% moisture (as-received), 23% ash yield, and 2.9% total sulphur and 4167 kcal/kg gross calorific value on an air-dried basis. Mean random reflectance values of huminite have an average of 0.34%, and show no differences across the coalfield. Coal ranking has been determined by the fuzzy Mamdani model. During processing, fixed carbon content (FCC), volatile matter values (VMV), calorific value (CV), and vitrinite/huminite reflectance (Ro %) parameters are inputs, and a total of 10 rules are used for the model. Modeling results are consistent with the previous studies.

Contributions to spatial landslide assessments in the Bâc tableland

The article is dedicated to studying landslide distribution patterns and to assessing landslide development using large-scale remote sensing data and GIS technologies. There were obtained digital models of elevation and landforms within key sectors. There were shown up dependency of the process development on morphometric characteristics of the slopes. Also, there were indentified different dependency rates for the conditions within different key sectors.

Integrated approach for determining spatio-temporal variations in the hydrodynamic factors as a contributing parameter in landslide susceptibility assessments

Although general approaches to the effect of water on the mechanisms causing landslides have been adopted, the work presented in this paper was carried out to quantify the landslide susceptibility variation in space and time, integrating the soil moisture distribution and routing (SMDR) model and landslide susceptibility concept. The approach proposed in the present study reflects the temporal effects of the saturation degree index (SDI) on landslide susceptibility as a new index to understand the effect of soil saturation. The topographic wetness index (TWI) is a conventional parameter that represents the relative wetness on landsliding. The new proposed landslide susceptibility approach is used in the study area to understand the effect of soil saturation and the emergence of the Derebaşı landslide in the study area. The comparative results of landslide susceptibility maps obtained from the new approach utilizing the proposed SDI and conventional TWI are remarkable. Accordingly, a new substantial method is proposed using the attainable monthly mean meteorological data to generate monthly landslide susceptibility maps. The results obtained for the Derebaşı landslide using the proposed method are validated with the other landslide that has occurred in the same watershed. The results revealed that the approach proposed in this study was compatible with the landslide mechanism in the study area and may help to express the water effect in landslide susceptibility analyses.

Landslide susceptibility mapping at Ovacık-Karabük (Turkey) using different artificial neural network models: comparison of training algorithms

This study aims to investigate the performances of different training algorithms used for an artificial neural network (ANN) method to produce landslide susceptibility maps. For this purpose, Ovacık region (southeast of Karabük Province), located in the Western Black Sea Region (Turkey), was selected as the study area. A total of 196 landslides were mapped, and a landslide database was prepared. Topographical elevation, slope angle, aspect, wetness index, lithology, and vegetation index parameters were taken into account for the landslide susceptibility analyses. Two different ANN structures, which were composed of single and double hidden layers, were applied to compare the effects of the ANN. Four different training algorithms, namely batch back-propagation, quick propagation, conjugate gradient descent (CGD), and Levenberg–Marquardt, were used for the training stage of the ANN models. Thus, eight different landslide susceptibility maps were produced for the study area using different ANN structures and algorithms. In order to assess the effects and spatial performances of the considered training algorithms on the ANN models, the relative operating characteristics (ROC) and relation value (rij) approaches were used. The susceptibility map produced by CGD1 has the highest AUC (0.817) and rij values (0.972). Comparison of the susceptibility maps indicated that CGD training algorithm is the slowest one among the other algorithms, but this algorithm showed the highest performance on the results.

Gis-Based Determination of Landslide Properties in Regional Scale: Karabuk Province (NW Turkey)

Abstract Natural hazards and their consequences are of great importance throughout the world. In Turkey, landslides constitute approximately 5% of the overall damage. The most important part of any landslide study is to extract landslide properties and database. In this study, Karabük city was selected as a study area which is known as one of the most landslide prone areas in Turkey. The study area contains the official borders of Karabük province. The area surrounded by the coordinates of 4518148N-4603891N and 424593E-512511E which has an areal extent of 4067 km square. The data of 1663 occurred landslides in Karabük, were digitized from 1/500.000 scale Turkey Landslide Inventory Map by considering the scarps with point vector format. Considering the literature, parameters of lithology, slope, topographical elevation, NDVI and aspect, which were frequently used among the researchers in landslide assessments, were produced and analyzed a GIS (Geographical Information System) platform. In order to perform analyses, the study area was divided into 62 watersheds. Then, lithology, slope, aspect, topographical elevation and NVDI characteristics of the region were automatically extracted by considering the landslide locations. In this type of study, GIS provides many advantages. For the next stages of landslide assessments such as susceptibility, hazard and risk, this stage provides important inputs and can be considered as the most important stage.