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Dilmen, Ömer

Profile URL
https://hdl.handle.net/20.500.12573/2717
Name Variants
Dilmen, Omer Dilmen, Ömer
Job Title
Arş. Gör.
Email Address
omer.dilmen@agu.edu.tr
Main Affiliation
02.03. İnşaat Mühendisliği
Status
Current Staff
Website
ORCID ID
0000-0002-7494-8625
Scopus Author ID
59366091800
Turkish CoHE Profile ID
84E90C72CC7763E8
Google Scholar ID
IVFwV5AAAAAJ
WoS Researcher ID
LPZ-2210-2024

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Now showing 1 - 3 of 3
  • Article
    Landsat 8 Görüntüleri ile Cheney Rezervuarında Bulanıklık Tahmini: Regresyon, Mars ve Treenet Yöntemlerinin Karşılaştırılması
    (2024-06-13) Nacar, Sinan; Bayram, Adem; Dilmen, Ömer; Gormus, Esra Tunc
    Rezervuarlardaki su kalitesi takibi, suyun kullanım amacına uygunluğu ve su canlılarının korunması için önemlidir ve su kalitesinin belirlenmesinde en yaygın kullanılan değişkenlerden biri de bulanıklıktır. Bu değişkenin takibinde kullanılan geleneksel yöntemlerin maliyetli ve zaman alıcı olması, su kalitesi takibi için daha ekonomik ve hızlı bir alternatif olan uzaktan algılama çalışmalarını ön plana çıkarmıştır. Bu çalışmada, Landsat 8 Operational Land Imager (OLI) görüntüleri kullanılarak Cheney Rezervuarında (Kansas, ABD) bulanıklık değişkenini tahmin edebilecek bir model kurulması amaçlanmıştır. Bu amaçla 99 Landsat 8 OLI görüntüsü, 2014-2022 yılları arasında rezervuarda takibi yapılan bulanıklık verileriyle aralarındaki zaman farkı 20 dakikadan az olacak şekilde eşleştirilmiştir. Tahmin modellerinin kurulmasında regresyon analizi, çok değişkenli uyarlanabilir regresyon eğrileri (MARS) ve TreeNet gradyan arttırma makinesi (TreeNet) yöntemleri kullanılmıştır. Kurulan modellerin performansları, ortalama karesel hata, ortalama karesel hatanın karekökü, ortalama mutlak hata ve Nash-Sutcliffe (NS) verimlilik katsayısı performans istatistikleri ile kıyaslanmıştır. MARS ve TreeNet yöntemlerinin tahmin gücünün test veri seti için birbirine eşit olduğu görülmüştür (NS = 0.61). En önemli parametrenin MARS yöntemi kullanılarak oluşturulan modelde B4/B1 (kırmızı/kıyı aerosol), TreeNet yöntemiyle oluşturulan modelde ise B4/B2 (kırmızı/mavi) olduğu belirlenmiştir.
  • Article
    Citation - WoS: 3
    Citation - Scopus: 3
    A Cleaner Demolition Scheduling Methodology Considering Dust Dispersion: A Case Study for a Post-Earthquake Region
    (Elsevier Sci Ltd, 2024-11) Dincer, Ali Ersin; Demir, Abdullah; Dilmen, Omer
    In the present century, pollution is a primary concern for billions, prompting governments to advocate cleaner ways of production. Demolition activity is often an indispensable solution for structures that have completed their economic life. However, there are no regulations for the scheduling of demolition, except those related to the method of demolition and ensuring worker safety. Older buildings incorporate hazardous materials, such as asbestos, silica, and lead. These materials not only carry inherent risks, but high levels of aerosols in the air also adversely affect health. In this study, a demolition scheduling method is proposed, considering the dust dispersion. This research is pioneering, providing a structured demolition schedule to minimize the impact on both humans and the environment. In the methodology, a dispersion model is used to calculate the region exposed to dust and the concentration distribution throughout that area. In addition to the dust effect map, a vulnerability map is created using Analytical Hierarchy Process (AHP), aiding in determining interrelations between vulnerable sites. Thus, the dust effect map is derived by considering both dust exposure and the vulnerability map. The region affected by dust and the concentration of dust vary based on wind characteristics. By knowing the dust effect maps for the site (or all subsites) during specified time periods, a schedule can be defined. As a case study, schedules causing the absolute minimum and optimum dust effect rates are established for Kahramanmaras,, , , T & uuml;rkiye which recently experienced a devastating earthquake. The findings of the case study show that the dust effect on humans and the environment is significantly reduced. Consequently, by adhering to the proposed scheduling plan, human exposure to demolition dust is minimized, resulting in reduced medical expenses even without increasing the cost of the demolition.
  • Article
    A Machine-Learning-Based Multi-Hazard GIS-AHP Framework for Wind Turbine Siting under Earthquake–Landslide Coupling
    (IOP Publishing Ltd, 2026) Dinçer, Ali Ersin; Demir, Abdullah; Öztürk, Şevki; Kalpakcı, Volkan; Dilmen, Ömer
    This study presents a machine-learning-based multi-hazard geographical information system (GIS)-analytical hierarchy process (AHP) framework for wind turbine siting that explicitly accounts for the coupled effects of earthquake and landslide hazards. The primary innovation lies in the development of a conditional weighting algorithm that integrates machine-learning-derived hazard assessments with structural engineering logic. Landslide susceptibility is first modeled using a random forest classifier trained on a comprehensive inventory of historical landslide data and 12 geo-environmental conditioning factors, producing a high-resolution susceptibility map with excellent predictive performance (AUC = 0.86). Feature importance analysis indicates that slope, hydrological indices, and geological conditions are the dominant controls on landslide occurrence. This data-driven map is then integrated with earthquake hazard zones and additional environmental and technical constraints within a GIS-AHP framework to generate a comprehensive wind turbine suitability assessment. Results show that explicitly accounting for earthquake-landslide coupling leads to a nearly 20% reduction in high and very high suitability areas, accompanied by an expansion of low and moderate suitability zones, highlighting the limitations of single-hazard planning approaches. The main contribution of this study lies in advancing renewable energy planning through the explicit integration of interdependent natural hazards, demonstrating how earthquake-resistant foundation strategies can simultaneously mitigate landslide risks.