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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
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    Simplifying complexity: the novel degradation index for urban stream landscapes-case of Tekirdag
    (Springer, 2024) Ozsahin, Emre; Ozdes, Mehmet
    This study aims to examine the degradation process of urban stream landscapes in Tekirda & gbreve; by utilizing a newly developed degradation index (DI) that effectively defines degradation. The DI considers the vital factors that affect urban stream landscapes, making it possible to determine the level of degradation and resulting environmental quality. This index aims to provide valuable insight into the degradation of urban stream landscapes with a minimal amount of independent variables and training samples. The key enhancements of the DI include its clear computation of degradation value, ease of replication, and overall objectivity. The results indicate that the most substantial change has been observed on impermeable surfaces between 2000 and 2020, resulting in a 5.18% increase in impervious surfaces. Furthermore, there has been a decrease by 4.67% in agricultural lands, highlighting a pronounced shift towards impervious surfaces. The total percentage of areas categorized as high and very high degradation categories increased by 1.54% over the study period. Additionally, the area classified as high degradation expanded from 135.91 to 375.42 hectares between 2000 and 2020. Notably, there was no land classified as very high degradation in 2000, whereas in 2020, it reached 38.15 hectares. The DI has been proven to provide better representational information on how human activity affects ecosystems compared to both the Human Influence Index and the Human Footprint Index which has been used for this purpose. As a valuable tool for urban planning strategies, the DI can provide decision-makers with a more precise depiction of degradation, aiding in the preservation of sustainable urban stream landscapes, particularly in rapidly urbanizing areas.
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    Spatiotemporal changes of soil erosion rate in the pre-and post-dam construction periods (1990-2018) determined through RUSLE and GIS: the case of the Kavak River Basin, East Thrace, Turkiye
    (Springer, 2024) Ozsahin, Emre
    In the last few decades, spatiotemporal changes in soil erosion rate have changed dramatically with the construction of dams, resulting in increased sedimentation in dam reservoirs and significant shoreline changes in deltas in rivers downstream. This article aims to investigate the effect of dam construction on the change of soil erosion rate through the RUSLE and GIS in the Kavak River Basin in Thrace Peninsula (Turkiye). The impact of the dam built in the source part of the river on soil erosion in the basin was analyzed by the conditions for characterizing the pre-and post-dam construction periods (1990-2018). The RUSLE soil erosion model and GIS were used to organize and evaluate the study data. Model estimation outputs were compared with the shoreline changes in the delta, SOC loss and dating of sediment deposits, and volume changes in the dam reservoir. Finally, the average soil loss (ASL) in the Kavak River Basin was demonstrated to be 5.31 t ha(-1) yr(-1) and 4.68 t ha(-1) yr(-1) in 1990 and 2018, respectively. In the same period, it was determined that there was a -80 m shoreline regression in the delta. According to the study's results, the erosion phenomenon may lead to a decrease in the economic life of the dam reservoir and a narrowing in productive agricultural areas in the delta.
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    The Spatial Distribution of Soil Loss in Alakir Creek Basin (Antalya) and Factors Influential on It
    (Univ Namik Kemal, 2016) Ozsahin, Emre
    Soil erosion, which is caused by water, is one of the most important environmental problems of Turkey. It increases every day as a result of the unconscious activities of people. This study aims to reveal soil loss in Alakir Creek basin and highlight the factors influential on differences between areas in terms of soil loss. GIS (Geographic Information Systems) based RUSLE (Revised Universal Soil Loss Equation) method was employed in the study. Various materials obtained from different resources were used. The obtained findings were evaluated via one-way analysis of variance and simple binary correlation. It was concluded that the basin has an average annual soil loss of 5.26 t ha(-1) y(-1). This is a significant finding because this value is below Turkey's average (6.14 t ha(-1) y(-1)). On the other hand, it was found that very low (Risk 1) erosion risk is effective in most of the basin area (61.9%). Also, the statistical analysis results indicated that a significant difference and a positive (+) relationship exist between the erosion risk in the basin and the variables. Certain measures should be taken to prevent or minimize erosion across the basin. In this context, firstly the current erosion areas should be announced as erosion control areas and taken under control. This study showed that erosion practices supported by GIS and statistical analyses yield sounder results.