Landslide susceptibility mapping of Cekmece area (Istanbul, Turkey) by conditional probability

International audienceAs a result of industrialization, throughout the world, the cities have been growing rapidly for the last century. One typical example of these growing cities is Istanbul. Today, the population of Istanbul is over 10 millions. Depending on this rapid urbanization, new suitable areas for settlements and engineering structures are necessary. For this reason, the Cekmece area, west of the Istanbul metropolitan area, is selected as the study area, because the landslides are frequent in this area. The purpose of the present study is to produce landslide susceptibility map of the selected area by conditional probability approach. For this purpose, a landslide database was constructed by both air ? photography and field studies. 19.2% of the selected study area is covered by landslides. Mainly, the landslides described in the area are generally located in the lithologies including the permeable sandstone layers and impermeable layers such as claystone, siltstone and mudstone layers. When considering this finding, it is possible to say that one of the main conditioning factors of the landslides in the study area is lithology. In addition to lithology, many landslide conditioning factors are considered during the landslide susceptibility analyses. As a result of the analyses, the class of 5?10° of slope, the class of 180?225 of aspect, the class of 25?50 of altitude, Danisment formation of the lithological units, the slope units of geomorphology, the class of 800?1000 m of distance from faults (DFF), the class of 75?100 m of distance from drainage (DFD) pattern, the class of 0?10m of distance from roads (DFR) and the class of low or impermeable unit of relative permeability map have the higher probability values than the other classes. When compared with the produced landslide susceptibility map, most of the landslides identified in the study area are found to be located in the most (54%) and moderate (40%) susceptible zones. This assessment is also supported by the performance analysis applied at end of the study. As a consequence, the landslide susceptibility map produced herein has a valuable tool for the planning purposes

CONSIDERATIONS ON THE USE OF SENTINEL-1 DATA IN FLOOD MAPPING IN URBAN AREAS: ANKARA (TURKEY) 2018 FLOODS

Flood events frequently occur due to -most probably- climate change on our planet in the recent years. Rapid urbanization also causes imperfections in city planning, such as insufficient considerations of the environmental factors and the lack of proper infrastructure development. Mapping of inundation level following a flood event is thus important in evaluation of flood models and flood hazard and risk analyzes. This task can be harder in urban areas, where the effect of the disaster can be more severe and even cause loss of lives.With the increased temporal and spatial availability of SAR (Synthetic Aperture Radar) data, several flood detection applications appear in the literature although their use in urban areas so far relatively limited. In this study, one flood event occurred in Ankara, Turkey, in May 2018 has been mapped using Sentinel-1 SAR data. The preprocessing of Sentinel-1 data and the mapping procedure have been described in detail and the results have been evaluated and discussed accordingly. The results of this study show that SAR sensors provide fast and accurate data during the flooding using appropriate methods, and due to the nature of the flood events, i.e. heavy cloud coverage, it is currently irreplaceable by optical remote sensing techniques.</p

Landslide susceptibility mapping at VAZ watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms

Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping. The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method. The success rate curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and 0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %), respectively. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning

Landslide susceptibility mapping using support vector machine and GIS at the Golestan province, Iran

The main goal of this study is to produce landslide susceptibility map using GIS-based support vector machine (SVM) at Kalaleh Township area of the Golestan province, Iran. In this paper, six different types of kernel classifiers such as linear, polynomial degree of 2, polynomial degree of 3, polynomial degree of 4, radial basis function (RBF) and sigmoid were used for landslide susceptibility mapping. At the first stage of the study, landslide locations were identified by aerial photographs and field surveys, and a total of 82 landslide locations were extracted from various sources. Of this, 75% of the landslides (61 landslide locations) are used as training dataset and the rest was used as (21 landslide locations) the validation dataset. Fourteen input data layers were employed as landslide conditioning factors in the landslide susceptibility modelling. These factors are slope degree, slope aspect, altitude, plan curvature, profile curvature, tangential curvature, surface area ratio (SAR), lithology, land use, distance from faults, distance from rivers, distance from roads, topographic wetness index (TWI) and stream power index (SPI). Using these conditioning factors, landslide susceptibility indices were calculated using support vector machine by employing six types of kernel function classifiers. Subsequently, the results were plotted in ArcGIS and six landslide susceptibility maps were produced. Then, using the success rate and the prediction rate methods, the validation process was performed by comparing the existing landslide data with the six landslide susceptibility maps. The validation results showed that success rates for six types of kernel models varied from 79% to 87%. Similarly, results of prediction rates showed that RBF (85%) and polynomial degree of 3 (83%) models performed slightly better than other types of kernel (polynomial degree of 2 = 78%, sigmoid = 78%, polynomial degree of 4 = 78%, and linear = 77%) models. Based on our results, the differences in the rates (success and prediction) of the six models are not really significant. So, the produced susceptibility maps will be useful for general land-use planning

Evaluation of geo-mechanical properties of very weak and weak rock materials by using non-destructive techniques: Ultrasonic pulse velocity measurements and reflectance spectroscopy

The main purpose of this study is to evaluate the geo-mechanical properties of very weak and weak rock materials by using ultrasonic pulse velocity measurements and considering specifically detailed mineralogical compositions. For the purpose, P-wave velocity (V-p) measurements of 66 core samples of the sedimentary rocks including claystones and mudstones collected from Firuzkoy area of Istanbul (Turkey) were carried out. Unconfined Compressive Strength (UCS) tests were then conducted. The axial deformations recorded during the UCS tests were also evaluated and the elastic moduli of the rock materials (E-i) were calculated. Statistical relations were investigated between the values of P-wave velocity measurements and the UCS and E-i. In order to evaluate the effects of detailed mineralogical compositions on the prediction performances of the empirical equations V-p-UCS and V-p-E-i, the reflectance spectroscopy was introduced. 1035 spectral measurements were taken from smooth and fresh surfaces of the failed core samples. Different genetic rock types were defined according to crystal field effect and charge transfer absorptions of transition elements, and water and OH vibrational spectral diagnostics. The statistical relations were reinvestigated, and the individual empirical equations were reproduced for each genetic rock type. The mean values of the multiple coefficients of correlations (R) were obtained to be 0.904 and 0.916 for the equations of V-p-UCS and V-p-E-i. Considering the maximum values of R, the increment rates for the values of the explained variances reach up to 14.3% and 13.5% for the equations, respectively. Additionally, according to the results of the analysis of variance (ANOVA) evaluations, the empirical equations of V-p-UCS and V-p-E-i reproduced for each genetic rock type were found to be statistically significant at the significance level of 0.05. As a consequence, the non-destructive techniques V-p measurements and reflectance spectroscopy could be efficiently used together for the evaluation of the geo-mechanical properties of very weak and weak rock materials in particular. (c) 2013 Elsevier B.V. All rights reserved.Hacettepe University Scientific Research Unit Ankara, Turkey [07 01 602 001]The core samples were provided by the Hacettepe University Scientific Research Unit Ankara, Turkey with the project 07 01 602 001, and used with the permission of the project leader Prof. Dr. Candan Gokceoglu. The author also would like to gratefully thank to Mr. Murat Koruyucu and Dr. Engin O. Sumer for providing necessary conditions in order to implement spectroscopic measurements and XRD analyses

Probabilistic Risk Assessment in Medium Scale for Rainfall-Induced Earthflows: Catakli Catchment Area (Cayeli, Rize, Turkey)

The aim of the present study is to introduce a probabilistic approach to determine the components of the risk evaluation for rainfall-induced earthflows in medium scale. The Catakli catchment area (Cayeli, Rize, Turkey) was selected as the application site of this study. The investigations were performed in four different stages: (i) evaluation of the conditioning factors, (ii) calculation of the probability of spatial occurrence, (iii) calculation of the probability of the temporal occurrence, and (iv) evaluation of the consequent risk. For the purpose, some basic concepts such as "Risk Cube", "Risk Plane", and "Risk Vector" were defined. Additionally, in order to assign the vulnerability to the terrain units being studied in medium scale, a new more robust and more objective equation was proposed. As a result, considering the concrete type of roads in the catchment area, the economic risks were estimated as 3.6 x 10(6) (sic)-in case the failures occur on the terrain units including element at risk, and 12.3 x 10(6) (sic)-in case the risks arise from surrounding terrain units. The risk assessments performed in medium scale considering the technique proposed in the present study will supply substantial economic contributions to the mitigation planning studies in the region.WoSScopu

Indirect determination of weighted joint density (wJd) by empirical and fuzzy models: Supren (Eskisehir, Turkey) marbles

Correct block size assessment is the most important stage for rock quarry management. Although volumetric joint count (J(v)) and weighted joint density (wJd) were proposed for this purpose, simple prediction method for these indices is not encountered in literature. Due to the fact that some rock masses such as marbles contain less discontinuity, collection of representative amount of data from in situ line surveys for statistical assessments is highly difficult. For this reason, the main targets of the present paper are to apply photoanalysis approach for collecting additional discontinuity data and to obtain some simple statistical and fuzzy models for predicting weighted joint density to evaluate block size in engineering practice for marbles around Supren (Eskisehir, Turkey). In addition, a new and simple approach to predict volumetric decrease caused by chemical weathering is introduced. For these purposes, extensive field and photoanalysis studies were performed and the data obtained from both field and photoanalysis studies were assessed by regression and fuzzy approaches. The results revealed that the prediction performance of the fuzzy inference system is higher than that of the regression equation. (c) 2006 Elsevier B.V. All rights reserved