This research focuses on groundwater mapping for the Çumra and Beyşehir Basins in Konya, a semi-arid region in Turkey that plays a crucial role in agriculture and the food industry. Geographic information systems (GIS), the analytical hierarchical process (AHP), and the multi-population-based differential evolution algorithm (MDE) were combined to identify potential groundwater zones. Since direct data on groundwater presence are costly to obtain, thematic maps created from groundwater conditioning factors (such as aquifer, slope, permeability, alluvial soil, soil quality, lithology, precipitation, temperature, salinity, and stone density) can be used to estimate groundwater potential. In this study, these factors were assigned weights using the AHP technique in Model 1 and the MDE technique in Model 2. The TOPSIS (technique for order preference by similarity to ideal solution) method was then employed to simulate groundwater potential, using weights from both techniques. The performance metrics of both models were as follows: Model 1 (RMSE: 114.219, MSE: 13,046.091, and MAE: 99.663) and Model 2 (RMSE: 114.209, MSE: 13,043.785, and MAE: 99.652). The proposed method addresses issues of consistency and bias that might arise from relying on expert opinions through the use of heuristic techniques. Moreover, this approach, which does not require direct data on groundwater availability, enables the creation of accurate predictions while overcoming the challenges of obtaining expensive data in underdeveloped and developing countries. It provides a scientifically sound way to identify and conserve water resources, reducing drilling and other related costs in watershed management and planning.

Konya Basin, a key agro-ecosystem in Turkey, is increasingly vulnerable to desertification. This study assesses vegetation dynamics and climatic drivers between 2000 and 2025 using the Google Earth Engine platform. MODIS time-series (NDVI and SAVI) were analyzed to map long-term trends, and medium-resolution Landsat data identified degradation hotspots. Our results reveal an apparent contradiction. The basin shows a subtle greening trend, particularly in croplands (+0.0042 NDVI units yr⁻¹). However, medium-resolution Landsat data simultaneously indicate degradation hotspots covering a total of 3471.93 km². Croplands account for 21.4% of these areas, and about 70% occur below 1000 m, where groundwater-dependent irrigation is most intense. Climatic drivers clarify this dynamic. A significant warming trend of 0.05 °C yr⁻¹ (p = 0.0102) was detected, while vegetation correlated positively with precipitation (r = 0.50, p < 0.01) but showed no significant relationship with temperature (r = 0.09, p = 0.66). Spatial maps confirmed precipitation control in northern rainfed grasslands and temperature stress in irrigated southern plains. This multi-scale approach shows that basin-wide averages can be misleading, as modest greening coexists with local degradation. The findings emphasize the need for spatially explicit data to guide targeted land and water management policies to mitigate desertification risks in this vital region.

Taşınmaz değerlemesi kentsel alanda konumsal ve yapısal özelliklerin tarafsız ve objektif olarak değerlendirilmesini ifade etmektedir. Bu sürecin bilimsel tanımlanmasına ilişkin pek çok çalışma yapılmıştır. Literatürdeki çalışmalarda geleneksel, istatistiksel, çok kriterli karar analizleri ve yapay zekâ yöntemleri son yıllarda sıklıkla uygulanan yöntemlerdir. Günümüzde yapay zekâ yöntemleri taşınmaz değerleme sürecindeki çok sayıda taşınmaza ilişkin yapısal ve konumsal özellikleri analiz ederek ilişkilendirmekte ve taşınmazlara yönelik değer tahminleri gerçekleştirebilmektedir. Bu nedenle yapay zekâ yöntemleri taşınmaz değerleme sürecinin yönetilmesinde önemli bir araç konumundadır. Bu çalışmada, Türkiye’nin Niğde kentinde yapısal kriterlerine ilişkin verileri bulunan 1200 taşınmazın istatistiksel analiz tekniklerinden Lineer Regresyon ve Makine Öğrenimi yöntemlerinden Yapay Sinir Ağları, Regresyon Ağaçları, Destek Vektör Regresyon ve Gaussian Process Regresyon algoritmaları kullanılmıştır. Sonuçta yapay sinir ağları yöntemi ile eğitilen modele göre elde edilen sonuçların tahmin performansının en yüksek doğruluk (R2: %84.92, RMSE: 0.0608) sağladığı tespit edilmiştir. Çalışma, literatürden farklı olarak kent bütününde toplu olarak taşınmazların değerlemesiyle gerçekleştirilmiş ve değerlemede 1200 taşınmaza ilişkin kriterler karşılaştırılarak yüksek doğrulukla değer tahmini elde edilmiştir.

Optimizing vehicle placement in unmarked parking areas is essential for maximizing space efficiency, particularly in irregular and high-demand urban environments. This study investigates the optimal allocation of additional vehicles in spaces left unoccupied around parked cars by comparing seven heuristic optimization algorithms: Particle Swarm Optimization, Artificial Bee Colony, Gray Wolf Optimizer, Harris Hawks Optimizer, Phasor Particle Swarm Optimization, Multi-Population Based Differential Evolution, and the Colony-Based Search Algorithm. The experiments were conducted in two different parking areas, one designed for parallel parking and the other for perpendicular parking, under three scenarios allowing different levels of cars’ rotational flexibility. The results indicate that MDE consistently outperforms other methods in both speed and robustness, achieving the highest vehicle capacity. These findings provide a foundation for smart parking systems, enabling real-time optimization, reduced congestion, and improved urban mobility.

Prioritising properties for early review in dense urban renewal areas is a spatial multi-criteria decision problem in which physical condition, accessibility, environmental quality, and hazard exposure must be weighed before formal appraisal. This study develops a reproducible GIS-based multi-criteria decision analysis workflow for pre-transformation valuation support in Fatih Neighborhood, Esenler District, Istanbul, covering 54 independent property units. A 311-indicator geodatabase was screened for local discrimination, leaving 284 active sub-criteria under 10 retained main criteria. Expert pairwise judgments were converted into weights by six procedures compared under a common log-ratio objective, and the resulting TOPSIS rankings were tested for redundancy, benchmark divergence, judgment uncertainty, and structural robustness. The retained variables carry substantial overlapping spatial information, so the matrix does not provide hundreds of independent evidence signals, and equal within-group allocation concentrates influence in single-variable criteria. The leading priority tier nevertheless remains stable under calibrated weight variation and realistic judgment noise, whereas the wider rank order is sensitive to grouping, weighting, normalisation, and scoring choices. The workflow therefore separates robust priority signals from model-dependent rankings without interpreting the scores as market-value estimates, so that the intensity of field verification and appraisal can follow the reported stability of each unit.