Aykut Akgun

A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey

The main purpose of this study is to compare the use of logistic regression, multi-criteria decision analysis, and a likelihood ratio model to map landslide susceptibility in and around the city of İzmir in western Turkey. Parameters, such as lithology, slope gradient, slope aspect, faults, drainage lines, and roads, were considered. Landslide susceptibility maps were produced using each of the three methods and then compared and validated. Before the modeling and validation, the observed landslides were separated into two groups. The first group was for training, and the other group was for validation steps. The accuracy of models was measured by fitting them to a validation set of observed landslides. For validation process, the area under curvature (AUC) approach was applied. According to the AUC values of 0.810, 0.764, and 0.710 for logistic regression, likelihood ratio, and multi-criteria decision analysis, respectively, logistic regression was determined to be the most accurate method among the other used landslide susceptibility mapping methods. Based on these results, logistic regression and likelihood ratio models can be used to mitigate hazards related to landslides and to aid in land-use planning.

Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey)

In this study, landslide risk assessment for Izmir city (west Turkey) was carried out, and the environmental effects of landslides on further urban development were evaluated using geographical information systems and remote sensing techniques. For this purpose, two different data groups, namely conditioning and triggering data, were produced. With the help of conditioning data such as lithology, slope gradient, slope aspect, distance from roads, distance from faults and distance from drainage lines, a landslide susceptibility model was constructed by using logistic regression modelling approach. The accuracy assessment of the susceptibility map was carried out by the area under curvature (AUC) approach, and a 0.810 AUC value was obtained. This value shows that the map obtained is successful. Due to the fact that the study area is located in an active seismic region, earthquake data were considered as primary triggering factor contributing to landslide occurrence. In addition to this, precipitation data were also taken into account as a secondary triggering factor. Considering the susceptibility data and triggering factors, a landslide hazard index was obtained. Furthermore, using the Aster data, a land-cover map was produced with an overall kappa value of 0.94. From this map, settlement areas were extracted, and these extracted data were assessed as elements at risk in the study area. Next, a vulnerability index was created by using these data. Finally, the hazard index and the vulnerability index were combined, and a landslide risk map for Izmir city was obtained. Based on this final risk map, it was observed that especially south and north parts of the Izmir Bay, where urbanization is dense, are threatened to future landsliding. This result can be used for preliminary land use planning by local governmental authorities.

Mapping erosion susceptibility by a multivariate statistical method: A case study from the Ayvalık region, NW Turkey

Erosion is one of the most important natural hazard phenomena in the world, and it poses a significant threat to Turkey in terms of land degredation and desertification. To cope with this problem, we must determine which areas are erosion-prone. Many studies have been carried out and different models and methods have been used to this end. In this study, we used a logistic regression to prepare an erosion susceptibility map for the Ayvalik region in Balikesir (NW Turkey). The following were our assessment parameters: weathering grades of rocks, slope gradient, structural lineament density, drainage density, land cover, stream power index (SPI) and profile curvature. These were processed by Idrisi Kilimanjaro GIS software. We used logistic regression analysis to relate predictor variables to the occurrence or non-occurrence of gully erosion sites within geographic cells, and then we used this relationship to produce a probability map for future erosion sites. The results indicate that lineament density, weathering grades of rocks and drainage density are the most important variables governing erosion susceptibility. Other variables, such as land cover and slope gradient, were revealed as secondary important variables. Highly weathered basalt, andesite, basaltic andesite and lacustrine sediments were the units most susceptible to erosion. In order to calculate the prediction accuracy of the erosion susceptibility map generated, we compared it with the map showing the gully erosion areas. On the basis of this comparison, the area under curvature (AUC) value was found to be 0.81. This result suggests that the erosion susceptibility map we generated is accurate.

Landslide susceptibility mapping by frequency ratio and logistic regression methods: an example from Niksar-Resadiye (Tokat, Turkey)

Turkey confronts loss of life and large economic losses due to natural disasters caused by its morphologic structure, geographical placement, and climate characteristics. The Kuzulu (Koyulhisar) landslide, which caused loss of life and property on 17th March 2005, occurred in an area near the country’s most important active fault, the North Anatolian Fault Zone. To mitigate and prevent landslide damages, prediction of landslide susceptibility areas based on probabilistic methods has a great importance. The purpose of this study was to produce a landslide susceptibility map by the logistic regression and frequency ratio methodologies for a 733-km2 area near the North Anatolian Fault Zone from the southeast of Niksar to Resadiye in Tokat province. Conditioning parameters, such as elevation, slope gradient, slope aspect, distance to streams, roads, and faults, drainage density, and fault density, were used in the analysis. Before susceptibility analysis, the landslides observed in the area were separated into two groups for use in analysis and verification, respectively. The susceptibility maps produced had five different susceptibility classes such as very low, low, moderate, high, and very high. To test the performance of the susceptibility maps, area under curve (AUC) approach was used. For the logistic regression method, the AUC value was 0.708; while for the frequency rate method, this value was 0.744. According to these AUC values, it could be concluded that the two landslide susceptibility maps obtained were successful.

GeoFIS: An integrated tool for the assessment of landslide susceptibility

Abstract In this study, requirements of landslide susceptibility mapping by a Mamdani fuzzy inference system (FIS) are identified, and a single standalone application (GeoFIS) is developed. GeoFIS includes two main open source libraries, one for GIS operations and the other for creating Mamdani FIS. As a result, it is possible to construct a landslide susceptibility map based on expert opinion, to visualize maps instantly and to measure model performance. GeoFIS supports all steps of the landslide susceptibility mapping process, starting from data deployment and ending with performance measurement. In GeoFIS, visual controls allow use of the inferred results and actual landslide occurrence information, and ROC–AUC values are calculated automatically. Moreover, a confusion matrix is produced, and alternative measurement schemes such as recall are suggested, to reveal those performance details not observable with ROC–AUC and to create trust in the inferred results. GeoFIS is applied to the Trabzon region of northern Turkey, and the recall and ROC–AUC values were .902 and .602, respectively.

Understanding the mechanism of slope failure on a nearby highway tunnel route by different slope stability analysis methods: a case from NE Turkey

The Arakli tunnel is located in the eastern Black Sea region where the most mass movement is observed in Turkey. Following the tunnel entrance portal excavations in basaltic tuffs on nearby the Konakonu residential area, an impending failure occurred. Because of the developed tension cracks and deformations on the ground, five houses and their gardens were damaged completely. The present study aims to investigate the mechanism of the failure. In order to do this, kinematic, limit equilibrium, and numerical stability analyses were carried out. Firstly, the kinematic analyses were performed taking into account the main joint sets for the slopes. The results of the kinematic analyses showed that planar and wedge failures were possible on the portal slope and no failure occurred on the cut slope. However, the limit equilibrium analysis showed that neither the planar nor wedge failures were expected to occur on the portal slope. The numerical stability analyses were performed to determine if circular failure is to occur in the slopes. The Phase2 programme was used in the numerical analyses, and the Strength Reduction Factors (SRF) of the slopes were determined. According to the numerical stability analyses, the failure mode for the portal slope is composite starting with a circular surface and following a linear surface and circular for the cut slope. The stability analyses indicated that the failure mechanism was not directly controlled by the joints and might be related to the low strength parameters of the rock mass and joints. Finally, precautions were determined to make the region stable using the Phase2 programme. After support installation, the SRF values for the portal and cut slopes increased from 1.21 to 1.63 and from 1.32 to 1.71, respectively. These results showed that the proposed support units prevent the effects of failure and were essential for the long-term stability.

Determination of coastal border line: an integrated approach for a part of Antalya coast (Turkey)

Sustainable development in coastal areas requires mapping of geological and geomorphological features of the area and precise determination of coastal border line. Such a study was carried out in the city of Antalya, which is undergoing intense infrastructural development in order to accommodate touristic activities. In the field, high water level, which is determined from moist-dry sand border and current berm crest, was taken as the indicator for shoreline, while vegetation line and topographic rises were taken as indicators of coastal border line. The geographic coordinates of all of the features were determined using differential Global Positioning System with application of “real-time kinematic measurement” technique. Quickbird satellite images of 2002 and 2009 were used in order to observe natural state of the coastal area before the constructions, to complete geomorphological map, and to confirm the coastal border line determined in the field. In addition, the natural vegetation line before the infrastructural development was determined using normalized difference vegetation index approach. Using grain size parameters for identification of coastal border line, high and low water levels were tested. The results showed that from low water level to coastal border line, D50 (median grain size) and coefficient of uniformity decrease while coefficient of curvature increases systematically. This study showed that use of only one approach to determine coastal border line is insufficient, and an integrated methodology is required.