Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey)

Landslide susceptibility mapping is one of the most critical issues in Turkey. At present, geotechnical models appear to be useful only in areas of limited extent, because it is difficult to collect geotechnical data with appropriate resolution over larger regions. In addition, many of the physical variables that are necessary for running these models are not usually available, and their acquisition is often very costly. Conversely, statistical approaches are currently pursued to assess landslide hazard over large regions. However, these approaches cannot effectively model complicated landslide hazard problems, since there is a non-linear relationship between nature-based problems and their triggering factors. Most of the statistical methods are distribution-based and cannot handle multisource data that are commonly collected from nature. In this respect, logistic regression and neural networks provide the potential to overcome drawbacks and to satisfy more rigorous landslide susceptibility mapping requirements. In the Hendek region of Turkey, a segment of natural gas pipeline was damaged due to landslide. Re-routing of the pipeline is planned but it requires preparation of landslide susceptibility map. For this purpose, logistic regression analysis and neural networks are applied to prepare landslide susceptibility map of the problematic segment of the pipeline. At the end, comparative analysis is conducted on the strengths and weaknesses of both techniques. Based on the higher percentages of landslide bodies predicted in very high and high landslide susceptibility zones, and compatibility between field observations and the important factors obtained in the analyses, the result found by neural network is more realistic.     

GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline,Hendek (Turkey)

A segment of natural gas pipeline was damaged due to landsliding near Hendek. Re-routing of the pipeline is planned, but it requires the preparation of a landslide susceptibility map. In this study, the statistical index (Wi) and weighting factor (Wf) methods have been used with GIS to prepare a landslide susceptibility map of the problematic segment of the pipeline. For this purpose, thematic layers including landslide inventory, lithology, slope, aspect, elevation, land use/land cover, distance to stream, and drainage density were used. In the study area, landslides occur in the unconsolidated to semi-consolidated clayey unit and regolith. The Wf method gives better results than the Wi method. Lithology is found to be the most important aspect in the study area. Based on the findings obtained in this study, the unconsolidated to semi-consolidated clayey unit and alluvium should be avoided during re-routing. Agricultural activities should not be allowed in the close vicinity of the pipeline.