Like many places around the world, the wildland–urban interface areas surrounding urban regions are subject to variable levels of fire risk, threatening the natural habitats they contact. This risk has been assessed by various authors using many different methods and numerical models. Among these approaches, machine learning models have been successfully applied to determine the weights of criteria in risk assessment and risk prediction studies. In Istanbul, data have been collected for areas that are yet to be urbanized but are foreseen to be at risk using geographic information systems (GIS) and remote sensing technologies based on fires that occurred between 2000 and 2021. Here, the land use/land cover (LULC) characteristics of the region were examined, and machine learning techniques, including random forest (RF), extreme gradient boosting (XGB), and light gradient boosting (LGB) models, were applied to classify the factors that affect fires. The RF model yielded the best results, with an accuracy of 0.70, an F1 score of 0.71, and an area under the curve (AUC) value of 0.76. In the RF model, the grouping between factors that initiate fires and factors that influence the spread of fires was distinct, and this distinction was also somewhat observable in the other two models. Risk scores were generated through the multiplication of the variable importance values of the factors and their respective layer values, culminating in a risk map for the region. The distribution of risk is in alignment with the number of fires that have previously occurred, and the risk in wildland–urban interface areas was found to be significantly higher than the risk in wildland areas alone.

The fires encountered in the buildings and facilities of industrial areas make up quite a small proportion of all fire cases. However, in terms of the heat release rate, size of the burned area, damage, and impact zone, such fires have a large impact as compared to other fire cases. All fires have risk in terms of propagation. However, since fires in industrial structures and plants have rather high levels, qualitatively and quantitatively, compared to residential fires and other types of fires, it should be regarded as necessary to research them extensively. In this study, fires that have broken out in various places around Turkey, such as in factories, cold storage plants, and manufacturing facilities, were investigated. We aimed to determine the level of risk of the occurrence of these fires in the environment. A large amount of detailed information gathered about these fires was analyzed. This information includes data about the causes of the fires, deformation data of various materials, data on technical problems, data on financial damage levels, and data on fire patterns. We found 40 of these fire cases in total, and the case data were used to calculate the risk scores with the Geographic Information System (GIS), Analytical Hierarchy Process (AHP), and Inverse Distance Weight (IDW) methods. For each fire case, places sensitive to damage and losses were assessed according to six main criteria. Buffer analysis maps were generated for the 40 fire cases, and the cases were ordered based on their overall risk scores. In this ordering, fire case number 21 was found in the riskiest region, and fire cases 32, 17, and 31 were found in the low-risk region. Fire case number 21 occurred in a factory used for manufacturing fabric. This factory works with high volumes of acrylic, polyester, and other raw materials. In addition, there are some structures in very close proximity. It was observed that fire cases could be well differentiated depending on the selected criteria in the model applied.

The key concepts availability and accessibility have been taken into consideration in urban studies as well as the health and social aspects of ageing. These terms are in close relation with the “active ageing”, “age-friendly city” and “liveable city” concepts. These concepts were created by the UN, the World Health Organization, and other institutions aiming to increase the quality of life of older individuals and to regulate their living environments in an optimal way for an active and independent life. Improving accessibility and availability of facilities for older people in urban areas is crucial to ensure that older people are able to meet their own needs as well as prevent their exclusion from society. The planning of cities that prevents the social exclusion of older people and provides an independent way of living is the main objective of the concept of liveable cities. From this point of view, this study aims to evaluate the existing opportunities in an urban area in the context of liveability. Out of the multi-criteria decision-making models, analytic hierarchy process (AHP) and geographic information systems (GIS) were therefore used in this research. Three main districts of Kepez, with the highest population of older individuals, have been chosen. According to the findings of the study, the weight of health services has the highest score compared to other criteria. The liveability scores and grading of the districts were obtained using the AHP matrix. In the study, it was concluded that a multi-criteria analysis could be carried out with quantitative data. The real land use and the close environment of the research area should also be considered in the evaluation process.

The aim of this study is to select new landfill sites in Antalya, Turkey which is the fifth biggest city according to the population, approximately 1.2 million. It is the first rank according to the population growth rate in Turkey. The city of Antalya is the touristic places and its climate is Mediterranean climate. Therefore, choosing landfill site is very crucial and it affects the future of the city. Existing landfill site will not be sufficient in near future. The study was implemented in the Aksu, Dosemealti, Kepez, Konyaalti, and Muratpasa districts in Antalya. The study area has almost 1980 km-square. Twelve parameters which are geology, fault lines, landslides, river, lake, elevation, slope, aspect, population, rain, road network, and temperature data were determined. These parameters were assessed using Analytical Hierarchy Process (AHP) method and the weights of criteria layers were computed. Then, the computed weights were checked by the consistency index and consistency ratio which were found as 0.12 and 8.5%, respectively. The consistency index for 12 parameters should be lower than 1.48 and the consistency ratio should be less than 10%. This means that the constructed AHP matrix is suitable for analysis. Finally, the most suitable areas for landfill were computed according to the weights achieved from AHP matrix. The results were obtained in a range between 0.39 and 0.90. When the obtained results were compared with the existing landfill site called Kizilli area, the AHP score was found as 0.62. Therefore, new landfill area(s) should be selected from the determined results.

In this study, the radioactivity contents of Carboniferous Zonguldak-Kozlu hard coals, Carboniferous Antalya-Pamucakyayla hard coals and Pliocene Konya-Karapınar lignites were determined, then compared with the limit values determined by the authorized institutions and their hazard indexes were determined. The range of measured activity concentrations was from 16.2 Bq·kg ⁻¹ to 227.6 Bq·kg ⁻¹ for ²³⁸ U, 20.6 Bq·kg ⁻¹ to 67.5 Bq·kg ⁻¹ for ²³² Th and 211.9 Bq·kg ⁻¹ to 515.5 Bq·kg ⁻¹ for ⁴⁰ K. The calculated mean absorbed gamma dose rate ( D ), radium equivalent activity (Ra _eq ) and annual equivalent dose (AED) were 105.7 nGy·h ⁻¹ , 227.9 Bq·kg ⁻¹ and 129.6 μSv·h ⁻¹ , respectively. Although ²³⁸ U and ²³² Th radionuclide activity concentrations are comparable to literature values, ⁴⁰ K activity concentrations were around three or four times higher than in UNSCEAR (2000) reports. As it poses a radiological risk, it is necessary to take the necessary precautions to reduce the negative effects on the environment and human health due to use of coals from Konya-Karapınar, Antalya-Pamucakyayla basins and to burn in a controlled manner.