Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2026Land Subsidence Susceptibility Mapping in Semi-Arid Regions Using PS-InSAR and Tree-Based Ensemble Machine Learning: A Case Study From Çumra, Türkiye
Bilgilioğlu, Burhan Baha
Land subsidence is an increasing environmental hazard in semi-arid agricultural basins where intensive groundwater abstraction, compressible geological units, and expanding land-use pressures interact. This study presents a PS-InSAR and machine-learning-based framework for land subsidence susceptibility mapping in the Çumra District of the Konya Closed Basin, Türkiye. A time-series PS-InSAR analysis was conducted using 172 Sentinel-1A ascending images acquired between 2018 and 2023, and 11,718 high-confidence subsidence points were extracted as the inventory dataset. Fourteen conditioning factors representing topographic, hydrogeological, lithological, environmental, and anthropogenic controls were integrated with eight tree-based ensemble models optimized using Optuna. Random Forest achieved the highest overall performance, with an accuracy of 0.9745, precision of 0.9562, recall of 0.9945, F1 score of 0.9750, and Cohen's Kappa of 0.9490, followed by Extremely Randomized Trees and XGBoost. Class-specific validation also confirmed the reliability of Random Forest in delineating the Very High Susceptibility class. Variable importance analysis identified land use, proximity to settlements, well density, and groundwater level change as the dominant predictors, emphasizing the role of groundwater-related anthropogenic pressure in subsidence development. The resulting susceptibility maps provide a spatial decision-support basis for groundwater management, land use planning, and subsidence risk mitigation.
- MakaleYalnız künye2023SPAN: 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.
- MakaleAçık Erişim2021Analysis of salt lake volume dynamics using sentinel-1 based SBAS measurements: A case study of lake Tuz, Turkey
Bilgilioğlu, Burhan Baha; Erten, Esra; Musaoğlu, Nebiye
As one of the largest hypersaline lakes, Lake Tuz, located in the middle of Turkey, is a key waterbird habitat and is classified as a Special Environmental Protection Area in the country. It is a dynamic lake, highly affected by evaporation due to its wide expanse and shallowness (water depth <40 cm), in addition to being externally exploited by salt companies. Monitoring the dynamics of its changes in volume, which cause ecological problems, is required to protect its saline lake functions. In this context, a spatially homogeneous distributed gauge could be critical for monitoring and rapid response; however, the number of gauge stations and their vicinity is insufficient for the entire lake. The present study focuses on assessing the feasibility of a time-series interferometric technique, namely the small baseline subset (SBAS), for monitoring volume dynamics, based on freely available Sentinel-1 data. A levelling observation was also performed to quantify the accuracy of the SBAS results. Regression analysis between water levels, which is one of the most important volume dynamics, derived by SBAS and levelling in February, April, July and October was 67%, 80%, 84%, and 95% respectively, for correlation in the range of 10–40 cm in water level, and was in line with levelling. Salt lake components such as water, vegetation, moist soil, dry soil, and salt, were also classified with Sentinel-2 multispectral images over time to understand the reliability of the SBAS measurements based on interferometric coherence over different surface types. The findings indicate that the SBAS method with Sentinel-1 is a good alternative for measuring lake volume dynamics, including the monitoring of water level and salt movement, especially for the dry season. Even though the number of coherent, measurable, samples (excluding water) decrease during the wet season, there are always sufficient coherent samples (>0.45) over the lake.