Wildfire susceptibility maps play a crucial role in preemptively identifying regions at risk of future fires and informing decisions related to wildfire management, thereby aiding in mitigating the risks and potential damage posed by wildfires. This study employs eXplainable Artificial Intelligence (XAI) techniques, particularly SHapley Additive exPlanations (SHAP), to map wildfire susceptibility in Izmir Province, Türkiye. Incorporating fifteen conditioning factors spanning topography, climate, anthropogenic influences, and vegetation characteristics, machine learning (ML) models (Random Forest, XGBoost, LightGBM) were used to predict wildfire-prone areas using freely available active fire pixel data (MODIS Active Fire Collection 6 MCD14ML product). The evaluation of the trained ML models showed that the Random Forest (RF) model outperformed XGBoost and LightGBM, achieving the highest test accuracy (95.6%). All of the classifiers demonstrated a strong predictive performance, but RF excelled in sensitivity, specificity, precision, and F-1 score, making it the preferred model for generating a wildfire susceptibility map and conducting a SHAP analysis. Unlike prevailing approaches focusing solely on global feature importance, this study fills a critical gap by employing a SHAP summary and dependence plots to comprehensively assess each factor’s contribution, enhancing the explainability and reliability of the results. The analysis reveals clear associations between factors such as wind speed, temperature, NDVI, slope, and distance to villages with increased fire susceptibility, while rainfall and distance to streams exhibit nuanced effects. The spatial distribution of the wildfire susceptibility classes highlights critical areas, particularly in flat and coastal regions near settlements and agricultural lands, emphasizing the need for enhanced awareness and preventive measures. These insights inform targeted fire management strategies, highlighting the importance of tailored interventions like firebreaks and vegetation management. However, challenges remain, including ensuring the selected factors’ adequacy across diverse regions, addressing potential biases from resampling spatially varied data, and refining the model for broader applicability.

In Turkey, applications and activities for the use, acquisition and arrangements of land tenure rights are executed under the authorisation and responsibilities of a variety of public institutions in terms of concerned legislative framework. In this paper, a case study has been implemented for Istanbul not only to evaluate the quality and usability of existing data from different institutions but also to get conclusions and recommendations for future land management works. In the context of this sample work of preparing an inventory for land management and land use in 1:25 000 scale and related synthesis and pre-feasibility analyses; the inventory praxis for urban and rural areas have been undertaken separately. Layer overlay and synthesis of the data in the database are the essential parts of analysing and extracting the outputs supporting land management activities for the purpose of taking correct decisions. In that manner, an overview map of Istanbul's natural and settlement areas has been extracted.

On February 6, 2023, two catastrophic earthquakes hit Turkey, causing significant damage and casualties. The main causes of these results are improper constructions and settlement placements. Regions where earthquake damage is typical are cities developed on fertile agricultural lands. Site selection for land suitability is one of the main application areas of the Geographical Information System (GIS). The Multi-Criteria Decision Approach (MCDA) has provided consistent and sufficient alternatives to decision-makers for land use planning activities for many years. This study focused on a large area covering five provinces with a total area of 36.409 km2 as the whole region represents 110.000 km2 and is challenging to manage in a single segment. Moreover, the heaviest damages were observed in the study area. Nine criteria were considered while using the weighted overlay function of the GIS via the Analytical Hierarchical Process (AHP). Agricultural and forest regions were excluded from the scope and the entire study area; 84.0% unsuitable, 0.4% low suitable, 1.5% moderately suitable, 11.3% suitable, and 3.2% highly suitable areas were identified for the post-disaster regeneration of settlements.

Bu araştırmanın kavramsal çerçevesi kentsel dönüşüm projelerinde kullanılan değerleme yöntemleri ve uygulama metotları üzerinedir. Çalışmanın esas amacı Türkiye’de kentsel dönüşüm projelerinde hak sahipliği katılım ve dağıtım değerlerinin belirlenmesi, değer belirlenirken kullanılan yöntemlerin doğruluğunun etkinliğinin ve uygulanabilirliğinin yorumlanması, ortaya çıkan yeni teknolojik yöntemlerin değerleme sürecine entegre edilerek daha hızlı ve doğru bir sonuç elde edilmesi, ayrıca imar uygulamaları veya arazi düzenleme çalışmaları kapsamında yapılan değerlemede istatiksel yöntemlerden faydalanılmasının kentsel dönüşüm projeleri üzerindeki etkisini incelemektir. Amaçlanan sonuca ulaşabilmek için Türkiye’de değerleme çalışmalarında kullanılan iki farklı yöntem olan bina bazlı değerleme ve toplu değerleme yöntemi karılaştırılarak, bu yöntemlerin uygulama alanlarının anlaşılması için Üsküdar ve Esenlerde uygulanan kentsel dönüşüm projeleri incelenmiştir. Bulgular iki farklı proje üzerinde sonuç değerleri ve hak sahiplerinin görüşleri ile ilişkilendirilmiştir.

In addition to the usual concerns that governments face, managing agriculture and agricultural areas is a significant issue. As a result, a country's agricultural production is highly dependent on its land potential. The transformation of agricultural areas into construction sites will be more attractive for the proprietors if they are not valued at their actual worth. This study applies and compares two different valuation methods, income and nominal land value approaches, to evaluate land in rural area, Cankiri Province, Catalelma Village, Central Turkey. In Turkey, income valuation method is typically applied for dealing with financial matters related to agricultural areas. On the other hand, one of the statistical methods, nominal valuation, was also used in the study area by applying the spatial analysis functions of geographical information system (GIS). In this study, four sample parcels representing three different land use capability classes (two irrigable, one medium, and one barren) were selected for comparison of two valuation methods. The results obtained during this research demonstrate that the nominal valuation approach produced more accurate results than the income method, and it may be used in the country's local administrative units due to available data.

İstanbul, deprem riski yüksek bir bölgede yer alması ve önüne geçilemeyen çarpık kentleşmesinden dolayı kentsel dönüşüme sürekli ihtiyacı artan bir kent haline gelmiştir. Doğru ve sürdürülebilir kentsel dönüşüm uygulamaları için sürekli çalışmalar yapılmaktadır. 1966 yılında yürürlüğe giren 775 Sayılı Gecekondu Kanunu’nda ilk kez tasfiye alanlarından bahsedilmiştir. Tasfiye alanlarında tekrar yapılaşma sağlanamadığı için bu alanlarda yer alan hak sahiplerinin başka alanlara taşınması gerekmektedir. Bu çalışmada, İstanbul’da kentsel dönüşüm uygulanamayan alanlarda yer alan hak sahiplerinin yerleşebileceği rezerv alanlar tespit edilmiştir. Öncelikle yer seçim kriterleri analitik hiyerarşi yöntemi ile ağırlıklandırılarak, konumsal çakıştırma (weighted overlay) yöntemi kullanılmış ve olası rezerv alanlar belirlenmiştir.