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    Coastal Vulnerability Assessment of Thrace Peninsula: Implications for Climate Change and Sea Level Rise
    (Mdpi, 2023) Ozsahin, Emre; Ozdes, Mehmet; Ozturk, Mikayil; Yang, Di
    This study evaluates the susceptibility of the coastal regions on the Thrace Peninsula to sea-level rise (SLR) and the corresponding vulnerability to climate change. To achieve this, a high-resolution digital elevation model with a 5 m granularity was used to apply the Coastal Vulnerability Index, adjusted for region-specific coastal sensitivity factors. Various global mean sea-level rise scenarios were examined for the near-term (2020-2050), mid-term (2050-2100), and long-term (2100-2300) to assess the impact of SLR. The examination of the immediate consequences of SLR on coastal areas included the analysis of land cover characteristics in the near-term. Results indicate that the Thrace Peninsula is highly susceptible to natural and socio-economic hazards caused by SLR. The concentration of population and socio-economic activities in coastal regions is a primary contributing factor to this vulnerability. In addition, hydrodynamic models are used to enhance understanding of the effects of SLR. The study reveals limited preparedness for planned adaptations to SLR in the region. The data highlight the crucial necessity for policymakers, researchers, and stakeholders to collaborate in executing strategic interventions and proactive initiatives. Upholding the ecological, economic, and societal welfare of the Thrace Peninsula, as well as comparable areas, necessitates addressing both the vulnerability and resilience of immediate coastal regions.
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    Digital Soil Mapping (DSM) Using a GIS-Based RF Machine Learning Model: The Case of Strandzha Mountains (Thrace Peninsula, Türkiye)
    (Tech Science Press, 2024) Ozsahin, Emre; Sari, Huseyin; Erdem, Duygu Boyraz; Ozturk, Mikayil
    This study assessed and mapped the spatial distribution of soil types and properties developed under the forest cover of the Strandzha Mountains of T & uuml;rkiye. The study was conducted on a micro-scale in the riparian zone of the Balaban River, which characterizes the soils distributed in the mountainous area. The effect of environmental factors on the spatial distribution of soil types and properties was also determined. To gather data, soil sampling, laboratory analysis, data processing and mapping were sequentially performed. These data were analyzed using the Geographical Information System (GIS) based Random Forest (RF) machine learning technique. Digital Soil Mapping (DSM) was developed with satisfactory performance. DSM suggests that the factors affecting the spatial distribution of soil types and properties in the sample area are, from most important to least important, topography (50.77%), climate (28.14%), organisms (8.22%), parent material (7.24%) and time (5.63%). With the contributions of all these factors in different proportions, it was determined that soils belonging to the Entisol and then Inceptisol orders were the most widespread in the sample area. The study results revealed that the GIS-based RF machinelearning technique can be used as a reliable tool for the development of DSM in mountainous terrains.
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    Evaluation of Parameters Affecting Earthquake Damage Using a GIS-based Random Forests Machine Learning Model: The Case of the 6 February 2023 Kahramanmaras Earthquakes in Türkiye
    (Istanbul Univ, Fac Letters, Dept Geography, 2024) Ozsahin, Emre; Ozturk, Mikayil
    T & uuml;rkiye is a geographical feature with intense seismic activity due to its tectonic features. Despite such a high earthquake risk, the evaluation of parameters affecting earthquake damage is still very inadequate in T & uuml;rkiye. The aim of this study was to evaluate the parameters affecting earthquake damage in the 6 February 2023 Kahramanmaras earthquake, which caused the highest number of casualties in the history of the Republic of T & uuml;rkiye. Therefore, data were produced to understand the differences in the behavior of structures in the case of an earthquake hazard in different parts of T & uuml;rkiye. The study used sample data from 198,634 buildings with varying types of structural damage in residential areas where the earthquake had been felt. The relationship between these data and key factors causing structural damage was analyzed using a Geographic Information Systems (GIS)-based Random Forests (RF) Machine Learning (ML) model. As a result of this study, it was understood that the 6 February 2023 Kahramanmaras earthquakes caused structural damage as a result of different combinations of building age, local soil conditions, distance to fault lines, distance to the epicenter, ground slip velocity, maximum ground velocity, and soil liquefaction effect factors