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

Landslide-Triggering Factors in Korucak Subbasin, North Anatolian, Turkey

AbstractKorucak Creek Basin is located within upper course of the Yeşilırmak River Basin and southern Middle Karadeniz (Black Sea) section which is known to have the potential of landslide and flood risk. The purpose of identification of landslide-triggering factors is to highlight the regional distribution of potentially unstable slopes and to guide decision makers for regional planning purposes. We assessed morphometric parameters for landslide-triggering factors of Korucak Creek Basin using GIS (Geographical Information System). These parameters are Stream Power Index (SPI) and Compound Topographic Index (CTI). Moreover, slope and elevation values of the basin were classified and superposed over the geologic map. Landslide locations were identified from topographic maps and verified with field observation. The total catchment area of the basin is about 55 km2. More than half of the total basin is covered by metamorphic rock types such as schist, which has high permeability and weakness against erosion and is one of the main causes of the landslides. The results show that the main triggering factors are slope and lithology. Thus, northern and western of the Korucak subbasin are under the highest-risk landslide areas

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

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

A fuzzy classification routine for fine-grained soils

Soil classification is one of the most important stages in preliminary studies for design applications in geotechnical engineering. The classification of fine-grained soils is mostly determined using Casagrande's plasticity chart. However, owing to the factors affecting determination of liquid and plastic limits, uncertainties can arise in fine-grained soil classification when using this approach. The uncertainty increases particularly when the points on the chart fall on the lines that separate certain soil classes (A-line and/or 50% liquid limit line) or very close to these lines. In this study, a fuzzy classification routine is proposed to minimize these uncertainties. For this purpose, the spatial distances of the evaluation points on the chart from the lines were used as a controlling unit. The membership degrees that define the fuzzified soil (clay and/or silt), and plasticity (low and/or high plasticity), were evaluated by considering sigmoid functions. As a consequence, the soil types were established by aggregating fuzzified soil and plasticity using the fuzzy operators.Web of Science49434934

Medium-scale hazard mapping for shallow landslide initiation: the Buyukkoy catchment area (Cayeli, Rize, Turkey)

The main purpose of this study is to develop a new hazard evaluation technique considering the current limitations, particularly for shallow landslides. For this purpose, the Buyukkoy catchment area, located in the East Black Sea Region in the east of Rize province and the south of Cayeli district, was selected as the study area. The investigations were executed in four different stages. These were (1) preparation of a temporal shallow landslide inventory of the study area, (2) assessment of conditioning factors in the catchment, (3) susceptibility analyses and (4) hazard evaluations and mapping. A total of 251 shallow landslides in the period of 1955-2007 were recognised using different data sources. A 'Sampling Circle' approach was proposed to define shallow landslide initiation in the mapping units in susceptibility evaluations. To accomplish the susceptibility analyses, the method of artificial neural networks was implemented. According to the performance analyses conducted using the training and testing datasets, the prediction and generalisation capacities of the models were found to be very high. To transform the susceptibility values into hazard rates, a new approach with a new equation was developed, taking into account the behaviour of the responsible triggering factor over time in the study area. In the proposed equation, the threshold value of the triggering factor and the recurrence interval are the independent variables. This unique property of the suggested equation allows the execution of more flexible and more dynamic hazard assessments. Finally, using the proposed technique, shallow landslide initiation hazard maps of the Buyukkoy catchment area for the return periods of 1, 2, 5, 10, 50 and 100 years were produced

Preliminary Investigations On Flood Susceptibility Mapping in Ankara (Turkey) Using Modified Analytical Hierarchy Process (M-Ahp)

Scopu