Harita Mühendisliği Bölümühttps://hdl.handle.net/11494/2722024-03-29T02:39:43Z2024-03-29T02:39:43ZTracking surface and subsurface deformation associatedwith groundwater dynamics following the 2019 MirpurearthquakeKhan, Muhammad YounisSaralioğlu, EkremTurab, Syed AliMuhammad, Sherhttps://hdl.handle.net/11494/48762023-05-05T10:46:35Z2023-01-01T00:00:00ZTracking surface and subsurface deformation associatedwith groundwater dynamics following the 2019 Mirpurearthquake
Khan, Muhammad Younis; Saralioğlu, Ekrem; Turab, Syed Ali; Muhammad, Sher
The Mirpur Mw 5.8 earthquake on September 24, 2019, producedextensive liquefaction-induced surface deformation (LISD) in the sur-rounding villages. Due to the complexity of seismic hazards and theoccurrence of their effects on a large spatial scale, the resulting sur-face, and subsurface deformation are often poorly resolved. To coverspatially extended LISD, the PSInSAR technique provided subsidenceand uplift rate values ranging from 110 toþ145 mm/yr consistentwith the spatial distribution of the mapped liquefaction features. Themost prominent surface change occurred in Abdupur and Sang vil-lages. GPR measurements were conducted to map the near-surfacecracks produced by transported liquified sand into the shallow sub-surface layers and other liquefaction features (elevated groundwatertable, conductive clay pockets, fractures, sand dikes, and water-enriched zones). Thus, the GPR survey assisted in the reconstructionof these structural and hydrogeological features on the near surface.In addition, the highly vulnerable zones were identified and mappedusingspace-andground-basedremotesensingmeasurementssup-ported by the field observations. The results highlight the effective-ness of the proposed novel approach for detailed assessment of thecoseismic liquefaction-induced deformation on- and near-ground sur-faces by identifying areas prone to failure during earthquakes andthereby can help with hazard mitigation.
2023-01-01T00:00:00ZMulticriteria decision and sensitivity analysis support for optimal airport site locations in Ordu Province, TurkeyÇolak, H. EbruMemişoğlu Baykal, TuğbaGenç, Nihalhttps://hdl.handle.net/11494/48752023-05-05T10:36:08Z2023-01-01T00:00:00ZMulticriteria decision and sensitivity analysis support for optimal airport site locations in Ordu Province, Turkey
Çolak, H. Ebru; Memişoğlu Baykal, Tuğba; Genç, Nihal
In the study carried out in the Ordu province of Turkey, 16 criteria to be used in airport site selection were handled and evaluated by subjecting them to successive processes in the GIS environment. Each criterion was weighted with the AHP method, and a map of suitability for airport site selection was obtained in the GIS environment using these weights. The most suitable place for the airport in Ordu province was detected by evaluating the nine regions determined according to the resulting map. Then, the alternative areas preferred from the most suitable areas were evaluated according to the total scores from the classification intervals with a scenario where the criterion weights were assumed to be equal. Finally, sensitivity analysis was performed to identify those who played an active role in the site selection analysis or not. Thus the sensitivity of the site selection analysis was tested
2023-01-01T00:00:00ZMachine learning based forest fire susceptibility assessment of Manavgat district (Antalya), TurkeyAlkan Akıncı, HazanAkıncı, Halilhttps://hdl.handle.net/11494/47162023-02-23T07:58:03Z2023-01-01T00:00:00ZMachine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey
Alkan Akıncı, Hazan; Akıncı, Halil
This study primarily aims to produce forest fire susceptibility maps for the Manavgat district of Antalya province in
Turkey using different machine learning (ML) techniques. Forest fire inventory data were obtained from the General
Directorate of Forestry. The inventory data comprise a total of 545 forest fire ignition points during the years 2013–2021.
For model training and validation, 70% and 30% of these points were used, respectively. Average annual temperature,
average annual rainfall, aspect, distance to rivers, elevation, distance to settlements, forest type, distance to roads, land
cover, plan curvature, slope, solar radiation, tree cover density, topographic wetness index, and wind effect parameters
were used in the study. Multicollinearity analysis of these 15 factors showed that they are independent of each other. Treebased
ML models, namely, eXtreme gradient boosting (XGBoost), random forest, and gradient boosting machine, as well
as artificial neural networks (ANN) were used to produce forest fire susceptibility maps. The metrics of overall accuracy,
precision, recall, F1 score and area under the receiver operating characteristic curve (AU-ROC) were used to evaluate the
performance of the ML models. Based on our results, the XGBoost model revealed the most appropriate susceptibility
map that could be used for fire prevention measures.
2023-01-01T00:00:00ZRoad surface and inventory extraction from mobile LiDAR point cloud using iterative piecewise linear modelZeybek, MustafaBiçici, Serkanhttps://hdl.handle.net/11494/47092023-02-17T14:00:34Z2023-01-01T00:00:00ZRoad surface and inventory extraction from mobile LiDAR point cloud using iterative piecewise linear model
Zeybek, Mustafa; Biçici, Serkan
Roads are one of the main characteristics of cities, and their data should be updated periodically. In this study, a new automatic method is proposed for extracting road surface information and road inventory from a Mobile LiDAR System-based point cloud. The proposed method consists of four steps. First, a three-dimensional point cloud is acquired using the mobile LiDAR scanning raw data. To improve the extraction accuracy, irrelevant points are removed from the point cloud. Piecewise linear models are used in the third step to classify the road surface. Road geometric characteristics such as centerline, profile, cross-section, and cross slope are extracted in the final step. The manually obtained road boundary is compared with the extracted road boundary to assess the classification results. Completeness, correctness, quality, and accuracy measures are range from $97\%$ to $99\%$. When comparing these measures with previous studies, the proposed method produces one of the highest ones.
2023-01-01T00:00:00Z