Abstract
Urban areas may be affected by multiple hazards, and integrated hazard susceptibility maps are needed for suitable site selection and planning. Furthermore, geological–geotechnical parameters, construction costs, and the spatial distribution of existing infrastructure should be taken into account for this purpose. Up-to-date land-use and land-cover (LULC) maps, as well as natural hazard susceptibility maps, can be frequently obtained from high-resolution satellite sensors. In this study, an integrated hazard susceptibility assessment was performed for a developing urban settlement (Mamak District of Ankara City, Turkey) considering landslide and flood potential. The flood susceptibility map of Ankara City was produced in a previous study using modified analytical hierarchical process (M-AHP) approach. The landslide susceptibility map was produced using the logistic regression technique in this study. Sentinel-2 images were employed for generating LULC data with the random forest classification method. Topographical derivatives obtained from a high-resolution digital elevation model and lithological parameters were employed for the production of landslide susceptibility maps. For the integrated hazard susceptibility assessment, the Mamdani fuzzy algorithm was considered, and the results are discussed in the present study. The results demonstrate that multi-hazard susceptibility assessment maps for urban planning can be obtained by combining a set of expert-based and ensemble learning methods.
Highlights
Improved disaster management is an important focus locally and globally to reduce the losses caused by natural disasters [1]
Among the geology- and climate-related natural hazards [3], urban areas are mostly affected by landslides and floods
Multi-hazard assessment models can be generated by integrating multiple susceptibility assessment maps belonging to different types of natural hazards for a specific area
Summary
Improved disaster management is an important focus locally and globally to reduce the losses caused by natural disasters [1]. Production of regional landslide susceptibility maps can be difficult due to the requirement of actual data Such maps are essential for urban planning and disaster mitigation efforts carried out by governments. Since the input dataset was large and heterogeneous, the multi-criteria decision analysis (MCDA) method was used to evaluate the parameters [27] In all these studies, scores and weights provided by the experts had great importance, and it was stated by the researchers that they affected the accuracy of the results. Chen et al [28] considered debris flows and river and flash flooding to be common in one area These hazard types were examined in four scenarios (major, moderate, minor, and frequent events). The effects of hazard types were compared based on the results
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