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

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

An Artificial Neural Network Application To Produce Debris Source Areas of Barla, Besparmak, And Kapi Mountains (Nw Taurids, Turkey)

Various statistical, mathematical and artificial intelligence techniques have been used in the areas of engineering geology, rock engineering and geomorphology for many years. However, among the techniques, artificial neural networks are relatively new approach used in engineering geology in particular. The attractiveness of ANN for the engineering geological problems comes from the information processing characteristics of the system, such as non-linearity, high parallelism, robustness, fault and failure tolerance, learning, ability to handle imprecise and fuzzy information, and their capability to generalize. For this reason, the purposes of the present study are to perform an application of ANN to a engineering geology problem having a very large database and to introduce a new approach to accelerate convergence. For these purposes, an ANN architecture having 5 neurons in one hidden layer was constructed. During the training stages, total 40 000 training cycles were performed and the minimum RMSE values were obtained at approximately 10 000th cycle. At this cycle, the obtained minimum RMSE value is 0.22 for the second training set, while that of value is calculated as 0.064 again for the second test set. Using the trained ANN model at 10 000th cycle for the second random sampling, the debris source area susceptibility map was produced and adjusted. Finally, a potential debris source susceptibility map for the study area was produced. When considering the field observations and existing inventory map, the produced map has a high prediction capacity and it can be used when assessing debris flow hazard mitigation efforts.WoSScopu

Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: the Melen Gorge (NW Turkey)

In the international literature, although considerable amount of publications on the landslide susceptibility mapping exist, geomorphology as a conditioning factor is still used in limited number of studies. Considering this factor, the purpose of this article paper is to implement the geomorphologic parameters derived by reconstructed topography in landslide susceptibility mapping. According to the method employed in this study, terrain is generalized by the contours passed through the convex slopes of the valleys that were formed by fluvial erosion. Therefore, slope conditions before landsliding can be obtained. The reconstructed morphometric and geomorphologic units are taken into account as a conditioning parameter when assessing landslide susceptibility. Two different data, one of which is obtained from the reconstructed DEM, have been employed to produce two landslide susceptibility maps. The binary logistic regression is used to develop landslide susceptibility maps for the Melen Gorge in the Northwestern part of Turkey. Due to the high correct classification percentages and spatial effectiveness of the maps, the landslide susceptibility map comprised the reconstructed morphometric parameters exhibits a better performance than the other. Five different datasets are selected randomly to apply proper sampling strategy for training. As a consequence of the analyses, the most proper outcomes are obtained from the dataset of the reconstructed topographical parameters and geomorphologic units, and lithological variables that are implemented together. Correct classification percentage and root mean square error (RMSE) values of the validation dataset are calculated as 86.28% and 0.35, respectively. Prediction capacity of the different datasets reveal that the landslide susceptibility map obtained from the reconstructed parameters has a higher prediction capacity than the other. Moreover, the landslide susceptibility map obtained from the reconstructed parameters produces logical results

An Easy-To-Use Matlab Program (Mamland) For The Assessment Of Landslide Susceptibility Using A Mamdani Fuzzy Algorithm

In this study, landslide susceptibility mapping using a completely expert opinion-based approach was applied for the Sinop (northern Turkey) region and its close vicinity. For this purpose, an easy-to-use program, "MamLand," was developed for the construction of a Mamdani fuzzy inference system and employed in MATLAB. Using this newly developed program, it is possible to construct a landslide susceptibility map based on expert opinion. In this study, seven conditioning parameters characterising topographical, geological, and environmental conditions were included in the FIS. A landslide inventory dataset including 351 landslide locations was obtained for the study area. After completing the data production stage of the study, the data were processed using a soft computing approach, i.e., a Mamdani-type fuzzy inference system. In this system, only landslide conditioning data were assessed, and landslide inventory data were not included in the assessment approach. Thus, a file depicting the landslide susceptibility degrees for the study area was produced using the Mamdani FIS. These degrees were then exported into a GIS environment, and a landslide susceptibility map was produced and assessed in point of statistical interpretation. For this purpose, the obtained landslide susceptibility map and the landslide inventory data were compared, and an area under curve (AUC) obtained from receiver operating characteristics (ROC) assessment was carried out. From this assessment, the AUG value was found to be 0.855, indicating that this landslide susceptibility map, which was produced in a data-independent manner, was successful. (C) 2011 Elsevier Ltd. All rights reserved.Wo

Safety assessment of limestone-based engineering structures to be partially flooded by dam water: A case study from northeastern Turkey

Turkey has been faced with an escalating energy demand and recurring droughts within the last few decades. The construction of the BAGISTAS 1 Hydroelectric Power Plant Dam, one of the dams constructed in order to solve these problems, resulted in the partial submersion of a number of pre-existing railway bridges and retaining walls of the Divrigi-Ilic-Erzincan Railway System (NE Turkey). Before the accumulation of dam water, the structural safety of these 86-year-old infrastructures, which were constructed using carbonate rocks, were investigated under saturated conditions. The maximum uniaxial compressive strength (UCS) losses under saturated conditions, after the application of freezing-thawing, and after wetting-drying cycles, were determined. For the mortar samples obtained from a drill core, the wet-to-dry UCS ratio was determined to be 0.82, suggesting a high durability performance. The natural filling material, which was used behind the retaining structures and as the railway embankment, was classified as the selective filling material, representing the best conditions for a filling material. The samples representing the retaining wall and filling materials had very high slake durability indexes, showing that they are very durable under the effect of water. The closed-form analysis for partially submerged retaining walls indicated that the structures are safe against overturning and have permissible internal wall stresses under operational conditions. In addition, the structural safety assessment of a masonry bridge was investigated using 3D Finite Element Modeling (FEM) under the designed train and expected earthquake loads, in both dry and partly immersed conditions. The results of the study showed that the strength reduction of masonry in saturated conditions, under the raised waters of the newly constructed dam, has an insignificant effect on the submerged sections and does not pose any danger to the overall structural performance