Sürdürülebilir Kalkınma Amaçları

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Yazarlar

• Reis, Hatice Catal(2)
• Aliyazıcıoğlu, Şener(1)
• Balcik, Filiz Bektas(1)
• Barut, Abdulkadir(1)
• Bayram, Bulent(1)
• Bilgilioglu, Burhan Baha(1)
• Bozkurt, Salih(1)
• Bulut, Sinan(1)
• Demir, Nusret(1)
• Dervisoglu, Adalet(1)
• Goksel, Cigdem(1)
• Günen, Mehmet Akif(1)
• Günlü, Alkan(1)
• Ince, Abdulkadir(1)
• Jamil, Akhtar(1)
• Kalkan, Yusuf(1)
• Karacur, Soner(1)
• Kucuk, Turgay(1)
• Kurt, Ahmet Melih(1)
• Seker, Dursun Zafer(1)
• Soydaş, Şafak Sönmez(1)
• Topaloglu, Raziye Hale(1)
• Turk, Veysel(1)
• Yagmur, Nur(1)
• Çakır, Günay(1)
• Çam, Alper Veli(1)
• Öztürk, Kaşif Furkan(1)

• MakaleAçık Erişim2025Scopus SJRQ1/r/gumushane/14168
  Supervised classification-based framework for rock mass discontinuity identification using point cloud data

  Günen, Mehmet Akif; Öztürk, Kaşif Furkan; Aliyazıcıoğlu, Şener

  Mapping and evaluating rock mass discontinuities using point clouds is a critical task in mining, civil, and geological engineering. Rock discontinuities can significantly impact the integrity, strength, and stability of rock masses. The orientation of these discontinuities is also a key characteristic of the rock mass. Accurate orientation estimation from point clouds enables more precise predictions of rock mass behavior, leading to improved safety, more efficient excavation processes, reduced operational costs, and significant time savings. In this context, a supervised classification-based framework is proposed for calculating orientation parameters from point cloud. Supervised classification plays a crucial role in tasks where a model learns complex patterns from labeled data to accurately predict previously unseen instances. The proposed method consists of eight-steps, including: data collection, pre-processing (data filtering), adaptive neighborhood size selection (omnivariance-based), feature extraction (geometric features), feature selection (Minimum Redundancy Maximum Relevance method), classification (Support Vector Machine), clustering (connected component labeling), and plane fitting to calculate orientation parameters (dip angle and dip direction). The framework was applied to two real-world datasets and one synthetic dataset, which was tested in two different subsampled forms (random and uniform subsampling). The results statistically demonstrated that the technique was effective in detecting and characterizing rock mass discontinuities with high Accuracy, Recall, Precision, and F-Score values ranging from 94.64% to 99.57%. The deviations of the method in the measurements of the dip angle and the dip direction, compared to the manual measurements, range from 1% to 4%, indicating strong agreement with the manual measurements and the existing studies.

  AtıfScopus25·WoS24·OpenAlex26

  İncele
• MakaleAçık Erişim2025Scopus SJRQ1/r/gumushane/12519
  Efficiency analysis using the machine learning algorithms: model development and verification

  Soydaş, Şafak Sönmez; Kalkan, Yusuf; Çam, Alper Veli; Barut, Abdulkadir

  Recently, machine learning (ML) algorithms have been employed intensively in the field of finance as in all sectors. The issues such as financial distress prediction, bank credit risk calculation, etc., have been analyzed using ML algorithms. This study aimed to determine firm performance with the data envelopment analysis (DEA) method, sensitivity analysis, and ML algorithms and analyze the efficiency of companies via artificial neural networks (ANNs), support vector machines (SVMs), and logistic regression (LR) classification algorithms. In the study, first, 10 financial ratios were categorized into two parts, such as output and input, and efficiency scores were determined in MS Excel software. The obtained scores were included in the ML algorithm as a categorical dependent variable. Secondly, the data were extracted and included in the analysis software as 80% training and 20% test data, and the accuracy of ML algorithms was tested. Lastly, a comparative analysis of the estimation and classification algorithms of active and inactive companies was conducted. As a result of the analysis, the best classification prediction was seen as the ANN algorithm. SVM and LR algorithms also made an acceptable level of classification prediction. It was expected that the study would have contributed to the literature in terms of testing the companies whose efficiency scores were determined by the DEA method with ML techniques and determining which technique was more successful.

  AtıfScopus7

  İncele
• MakaleAçık Erişim2024Scopus SJRQ1/r/gumushane/12735
  Integration of a CNN-based model and ensemble learning for detecting post-earthquake road cracks with deep features

  Reis, Hatice Catal; Turk, Veysel; Karacur, Soner; Kurt, Ahmet Melih

  A safe and healthy infrastructure is essential for humanity. Maintenance and repair of roads, which are of great importance, especially in transportation, is essential. Roads can sometimes be damaged due to natural disasters or human factors. In this study, roads damaged as a result of the natural disaster and the 6 February 2023 Gaziantep-Kahramanmaras earthquakes, which had devastating/fatal consequences, were studied. In recent years, the use of machine learning methods in the automatic detection process of road cracks has become widespread. Here, deep learning architectures generally based on multilayer convolutional neural networks have become a popular solution, mainly thanks to their high discrimination power in visual data. In this study, it was aimed to automatically detect cracks on the roads in Gaziantep (Nurdagi) and Adiyaman provinces after the earthquake. The data set used in the experimental process consists of two categories: uncracked and cracked, and was collected by the researchers. ResNet152 deep learning architecture, traditional machine learning algorithms, and an ensemble learning (EL) model were used to classify cracks. Among the machine learning algorithms used in the experiments are support vector machine (SVM), K-nearest neighbors, Adaptive Boosting, and Naive Bayes. In the experimental process, ResNet152 architecture was used for feature extraction, and classification was carried out with machine learning algorithms. In addition, the EL method has been used to improve the success of machine learning algorithms. According to experimental results, the SVM algorithm and EL method gave the most successful results with 98.68% accuracy values. The proposed method can benefit experts in the automatic classification of road cracks. The study can contribute to activating rapid coordination and transportation networks in natural disasters that cause fatal and destructive causes, such as earthquakes.

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  AtıfScopus19·WoS16·OpenAlex13

  İncele
• MakaleAçık Erişim2023Scopus SJRQ1/r/gumushane/12631
  Modelling some stand parameters using Landsat 8 OLI and Sentinel-2 satellite images by machine learning techniques: a case study in Türkiye

  Bulut, Sinan; Günlü, Alkan; Çakır, Günay

  Remote sensing technologies have been extensively used in forest management in predicting stand parameters. The goal of this study is to use Landsat 8 and Sentinel-2 satellite images to estimate stand volume, basal area, number of trees, mean diameter, and top height. 180 temporary sample plots were taken in pure Crimean pine stands with varied structure. Reflectance, vegetation indices, and eight texture values were generated from Landsat 8 and Sentinel-2 satellite images. The stand parameters were modelled with the remotely sensed data using multiple linear regression, support vector machine, and deep learning techniques. The results showed that the support vector machine technique provided the highest level of model performance with 45° orientation for number of trees (R2 = 0.98, RMSE%=5.97) and 90° orientation for basal area (R2=0.91, RMSE%=15.22). The results indicated that the texture values presented better results than the reflectance and the vegetation indices in estimating the stand parameters.

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  AtıfScopus12·WoS12·OpenAlex16

  İncele
• MakaleAçık Erişim2021Scopus SJRQ2/r/gumushane/14621
  Satellite-derived shallow wetland bathymetry using different classification algorithms and datasets

  Dervisoglu, Adalet; Yagmur, Nur; Bilgilioglu, Burhan Baha

  Remote sensing (RS) effectively identifies, analyzes, and monitors wetlands. In addition to these two-dimensional studies, RS is used with several techniques in determining shallow water depths. The primary purpose of this study is to obtain shallow wetland bathymetry utilizing spectral reflections obtained at different water depths by field study and satellite images. Machine learning (ML) algorithms, which are widely used in remote sensing, are used in this study. Four algorithms were selected as random forest (RF), support vector machine (SVM), Neural Networks (NN), and Maximum Likelihood Classification (MLC). Since machine learning algorithms use training samples/datasets, the classification accuracy is directly related to selecting these data. The effect of pixel counts on classification was investigated by using two different training data set also. Duden (Kulu) Lake, which is a shallow wetland, was chosen as the study area. The Iterative Self-Organizing Data Analysis Technique (ISODATA) classification algorithm divided into as many clusters as possible was applied on Sentinel-2 multispectral images. All classes were rede-fined using measured spectral signatures and were created a bathymetric map. This map was used as reference data in creating training sets and the accuracy assessment of ML algorithms. When the water surface areas obtained from algorithms were compared with the bathymetric map and Normalized Difference Water Index, the best result was obtained with RF. According to the accuracy assessment results, it was seen that the number of training data affects the accuracy, and the best results were obtained with SVM and RF algorithms with training data containing more pixels (overall accuracy 93.87% and 92.64, kappa 0.89 and 0.87, respectively).

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  AtıfScopus1·WoS1·OpenAlex1

  İncele
• MakaleYalnız künye2019/r/gumushane/13490
  Mapping Istanbul quarry mining region using Landsat 8 OLI&TIRS data

  Balcik, Filiz Bektas; Topaloglu, Raziye Hale; Goksel, Cigdem

  Open mining operations have brought number of environmental challenges, which may result in soil erosion, dust, noise, water pollution, and severe impacts on biodiversity. Determination of the mining activities and its surrounding environment is a major issue in sustainable resource management. Many remote sensing indices have been developed and widely applied as an effective tool to determine different land surface types. The main objective of this study was to determine quarry mines and its surroundings land use and land cover (LULC) categories in the selected study area of Istanbul using different remote sensing indices including Tasseled Cap Transformation (Brightness, Greenness and Wetness) and Normalized Difference Bareness Index (NDBals). In this study, an open mining area in the northern Black Sea coastal part of Istanbul was selected as study area. Freely available Landsat 8 OLI & TIRs image was used to determine quarries and land cover/ land use categories in the concerned area. By using original bands of Landsat 8 OLI, TCT components and NDBal, three different data sets were created. Each image was then separately subjected to support vector machine classification and processed to identify and quantify LULC categories. The accuracy of classification results were evaluated in a comparitive manner using error matrix. Data set C had the highest overall accuracy and Kappa statistics that were calculated as 83.00 and 0.80, respectively.

  AtıfScopus1

  İncele
• MakaleYalnız künye2018/r/gumushane/10244
  Automatic extraction of sparse trees from high-resolution ortho-images

  Bayram, Bulent; Seker, Dursun Zafer; Jamil, Akhtar; Reis, Hatice Catal; Demir, Nusret; Bozkurt, Salih; Ince, Abdulkadir; Kucuk, Turgay

  Obtaining information about tree species distribution in agricultural lands is a topic of interest for various applications, such as tree inventory, forest management, agricultural land management, crop estimation, etc. This information can be derived from images obtained from modern remote sensing technology, which is the most economical way as compare to field surveys covering large geographic areas. Therefore, in this study, a new method is proposed for extraction and counting of sparse and regular distributed individual pistachio trees from agricultural areas on large scale from high-resolution digital ortho-photo maps, which were obtained using an airborne sensor (Ultracam-X). The input images were first smoothed by applying Gaussian filter to reduce the impact of noise. Normalized difference vegetation indices (NDVI) were then derived to obtain vegetation areas followed by Otsu's global thresholding algorithm to obtain candidate tree areas. Further, connected component (CC) analysis was applied to segregate each object. Morphological processing was performed to fill holes within tree objects and get smooth contours, which were obtained by using the Moore-neighbor tracing method (MNTM) for each CC, while geometrical constraints were applied to undermine possible non-tree elements from output image. To further improve the segmentation results for sparse trees, a new method was applied, called quadratic local analysis (QLA). QLA helped to segment the trees, which were missed by the Otsu method due to low contrast and resulted in improved accuracy (3-6%). The obtained results were compared with well-known support vector machine (SVM) classifier. Proposed method produced slightly better results (1-5%) than SVM for extraction of pistachio trees and obtained accuracy for QLA and SVM were 96 and 91% for region 1, while 91 and 90% for region 2 respectively.

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  AtıfScopus3·WoS2·OpenAlex3

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