İlgi alanları

Sürdürülebilir Kalkınma Amaçları

• 4
• 1
• 1
• 1
• 1

• MakaleYalnız künye2025/r/gumushane/19769
  Vision Foundation Models and Rule-Based Approaches for Roof Surface Segmentation and Photovoltaic Potential Analysis in Urban Areas

  Özdemir, Samed; Yavuzdoğan, Ahmet

  This study presents two methods for rapidly and effectively determining the photovoltaic (PV) potential of building roofs in urban areas using aerial photographs and point cloud data. In the first method, the Segment Anything Model (SAM) and Contrastive Language Image Pre-Training (CLIP) models are used to detect roof surfaces and obstacles from aerial photographs. In the second method, the Random Sample Consensus (RANSAC) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms are employed to identify roof surfaces from Light Detection and Ranging (LiDAR) point clouds. Through the first proposed method, the performance of current deep learning approaches in 2.5D PV potential analysis is investigated, while the second approach examines the performance of 3D PV potential analysis compared to the 2D approach. In PV potential analysis, the Photovoltaic Geographical Information System (PVGIS) Application Programming Interface (API) was utilized. The analysis is conducted based on roof parameters obtained through both proposed methods. In building detection, the first approach achieved an Intersection over Union (IoU) score of 94.29%, whereas the second approach attained an IoU score of 91.23%.

  İncele
• MakaleAçık Erişim2025/r/gumushane/13946
  Advancing sea level anomaly modeling in the black sea with LSTM Auto-Encoders: A novel approach

  Yavuzdoğan, A.; Tanir Kayıkçı, E.

  Rising sea levels pose significant risks to coastal communities and ecosystems. Accurate modeling of sea level changes is crucial for effective environmental management and disaster mitigation. Machine learning methods are emerging as an important asset in improving sea level predictions and understanding the impacts of climate change. Especially, Long Short-Term Memory (LSTM) models have emerged as a powerful tool for sea level anomaly modeling, but there is an increasing need for more advanced models in this area. This study enhances existing methodologies by introducing a novel approach using an LSTM Auto-Encoder model, designed to compress input data into a lower-dimensional latent space before reconstructing it, thereby capturing complex temporal dependencies and anomalies effectively. We compared LSTM Auto-Encoder model performance with that of a Stacked LSTM network, which learns complex temporal patterns through multiple layers, and a traditional damped-persistence statistical model. Our results demonstrate that the LSTM Auto-Encoder model not only outperformed these models in predicting sea level anomalies across various lead times but also exhibited superior generalization capabilities across both satellite altimeter and in-situ data. These findings highlight the potential of the LSTM Auto-Encoder model as a powerful tool in coastal management and climate change studies, underscoring the critical role of advanced machine learning techniques in enhancing our predictive abilities and informing disaster preparedness strategies.

  • 
  İncele
• MakaleYalnız künye2023/r/gumushane/12753
  SPAN: An open-source plugin for photovoltaic potential estimation of individual roof segments using point cloud data

  Özdemir, Samed; Yavuzdoğan, Ahmet; Bilgilioğlu, Burhan Baha; Akbulut, Zeynep

  Decentralized solar PhotoVoltaic (PV) is one of the most promising energy sources for cities and individuals pursuing energy self-sufficiency. Especially, the already available rooftop surfaces are a major contributor to push for rooftop mounted PV systems. However, accurate PV potential estimation of individual buildings is still a challenging task since many parameters must be considered such as meteorological factors, panel technology, geographical location, available roof surface area, surface azimuth and tilt angle. In this study, we created an efficient approach that can be used for roof surface's PV potential estimation based on point cloud data and capable of processing various scales from single building to city scale. In the proposed approach, each roof surface's features were utilized for PV potential estimation by employing the PVGIS database. PV potential estimation was carried out on daily, monthly, and annual periods to provide a better estimation. Also, we developed a flexible and easy to use open-source plugin based on the QGIS software for rooftop mounted PV potential estimation capable of estimating every roof surface's PV potential. The method was tested on 80 buildings selected from ROOFN3D dataset. The proposed approach achieved an overall accuracy of 84% and an F1 score of 0.92.

  • 
  • 
  • 
  • 
  İncele
• MakaleAçık Erişim2021/r/gumushane/17524
  Forecasting instantaneous sea level changes with machine learning methods in Aegean Sea

  Kayıkçı, Emine Tanır; Yavuzdoğan, Ahmet

  Forecasting instantaneous sea-level is of great importance in terms of determination of geodetic vertical datum and updating, conservation of coastal areas, monitoring coastal ecosystems, maintenance, and planning of coastal structures, monitoring of climate change effects. Traditional methods used for instantaneous sea level estimation are often based on linear assumptions. However, contributors to sea levels are very various and their effects vary from region to region. Generally, they have complex and nonlinear dependence structures. Therefore, nonlinear sea-level cannot be determined with high precision using linear models. Recently, machine learning prediction methods have been frequently used in the modelling of complex dependency structures between variables. Within the scope of this study, to predict the instantaneous sea level with high accuracy and to compare linear estimation methods with nonlinear estimation methods, the Multiple Linear Regression (MLR) linear model, Support Vector Regression (SVR) non-linear model, and Random Forest Regression (RFR) non-linear model algorithms were used, and their prediction performances were compared. As a result of the study, the highest prediction performance for instantaneous sea level was obtained with RFR, and the lowest prediction performance was obtained with the MLR method. As a result, it has been shown that instantaneous sea level can be predicted with high precision using RFR with the features used in this study, and the linear prediction models are insufficient in modelling the complex dependency structure of instantaneous sea level.  

  İncele
• MakaleYalnız künye2021/r/gumushane/13316
  A copula approach for sea level anomaly prediction: a case study for the Black Sea

  Yavuzdoğan, Ahmet; Tanır Kayıkçı, Emine

  Forecasting future sea levels is of great importance in terms of the conservation of coastal areas, monitoring and forecasting coastal ecosystems, and the maintenance and planning of coastal structures. In addition, highly accurate sea level forecasts allow adequate water management policies and coastal infrastructures to be developed. Today, many methods, such as harmonic analyses, artificial neural networks and support vector machines, are used to predict sea level anomalies. In this study, a novel approach based on Copula functions is presented for the prediction of sea level anomalies. The primary purpose of this study is to examine the applicability and capability of Copula-based prediction models in predicting short-term variations in the sea level. The minimum 95% correlations and minimum 22 mm RMSE values in sea level anomaly predictions during the testing period indicate that the Copula approach can be a powerful tool in the prediction of sea level anomalies.

  • 
  İncele
• MakaleAçık Erişim2020/r/gumushane/17891
  Ege Denizi Bağıl Deniz Seviyesi Değişimlerinde Kapula Fonksiyonları İle Trend Analizleri

  Kayıkçı, Emine Tanır; Yavuzdoğan, Ahmet

  İklim değişikliği başta olmak üzere birçok nedenden dolayı global ölçekte deniz seviyesinde meydana gelen artışlar, kıyı ekosistemlerinietkilemekte ve kıyı yerleşimlerini tehdit etmektedir. Jeodezik açıdan uzun dönemli deniz seviyesi gözlemleri düşey kontrol ağlarınındatumunun belirlenmesi açısından önemlidir. İstatistik, ekonomi, aktüerya analizi, meteoroloji, hidroloji gibi birçok alanda farklıdeğişkenler arasındaki ilişkileri araştırmak için kullanılan Kapula fonksiyonları değişkenlerin ve hatalarının dağılımındanetkilenmeyen, lineer ve lineer olmayan ilişkileri yansıtan, lokal bağımlılıkları da gösterebilen korelasyon yapısıyla lineer korelasyonkatsayısına göre oldukça elverişli bir korelasyon yapısı sunabilmektedir. Bu çalışmada, Ege Denizi kıyısında bulunan 5 mareografistasyonundan elde edilen 1990-2008 yılları arasında aylık deniz seviyesi ve deniz yüzey sıcaklığı ortalamaları verileri ile kıyılardakiuzun dönemli bağıl deniz seviyesi değişimleri ve bu değişimlerin deniz yüzey sıcaklığı ile ilişkisi Kapula Fonksiyonları ile incelenmiştir.Bu çalışma kapsamında ilk defa sunulan kapula temelli trend analiz yöntemi ile deniz seviyesi ve deniz yüzey sıcaklığı zaman serilerindetrend analizleri gerçekleştirilerek elde edilen trendler regresyon modelleri ile karşılaştırılmıştır. Ege denizinde 1990-1998 yıllarıarasında deniz seviyesinin oldukça hızlı bir şekilde arttığı, 1999-2008 yılları arasında ise deniz seviyesinin düştüğü görülmüştür. 1990-1998 yılları arasında deniz seviyelerindeki artışın deniz yüzey sıcaklıklarındaki sıcaklık artışlarından kaynaklandığı, 1999-2008 yıllarıarasındaki deniz seviyesindeki azalmaların da deniz yüzey sıcaklıklarındaki soğumalardan kaynaklandığı değerlendirilmiştir.

  • 
  • 
  İncele