People are with overall nominal temperature, precipitation, humidity and where in certain ranges of environmental conditions such as wind they feel healthy and dynamic. In the appropriate range for the people of these values it is called the bioclimatic comfort. When bioclimatic comfort area is in the range of fair value would bother people in the area and want to get away from the area. Hence bioclimatic comfort areas used for the purpose of tourism is very important. In this study, Kastamonu-Cide bioclimatic comfort is done mapping of the coastline and thus aimed at building pad similar studies of a similar nature in the coastal areas. For this purpose, the region's climate data Cide is located; equivalent temperature according to the physiological index is bioclimatic comfort maps were prepared. To determine the structure of the field is bioclimatic comfort is collected climatic data from meteorological stations. The obtained data were evaluated by means of Rayman 1.2 and geographic information system (GIS) is used to produce thermal perception map with the help of software. According to the psychological equivalent temperatures As a result, the most appropriate time and area for outdoor recreation activities have been identified by thermal perception maps.

Agricultural land cover has changed over time, and monitoring these changes has become an effective tool in development processes by linking them to ecological and socio-economic issues. In this context, the main hypothesis of the study is that “determining land use changes spatially and temporally using CORINE Land Cover data is crucial in development-oriented planning processes.” The study analyzes changes in agricultural land use based on CORINE land cover classes between 1990 and 2018 in the districts of Saray, Şarköy, and Hayrabolu in Tekirdağ Province, which have low levels of socio-economic development. The research aims to answer the following questions: during which periods did significant changes occur in agricultural areas in Saray, Şarköy, and Hayrabolu? What are the total rates of increase or decrease in these areas? Into what types of land have agricultural are+as significantly transformed? The methodology was developed using Geographic Information Systems (GIS)-based maps, graphs, and tables. The findings reveal the spatial and temporal dimensions of changes in agricultural land use and land cover between 1990 and 2018. The data obtained are anticipated to provide a concrete foundation for developing rural development policies and ensuring the sustainable use of natural resources.

This is study is based with the support of RS-GIS technology on the land use of Samsun Center, as well as the coastal districts of Ilkadım,Atakum,Bafra Plain, through the processing and interpretation of satellite images in the summer months of 2000,2010,2020. Spatial and temporal variability properties of LU/LC were determined using MLC algorithm, controlled classification approach. The predictive values of the LU/LC change that will occur in 2030, calculated with the MLP‑ANN model based on Machine Learning algorithms and mapped with the QGIS 3.16 program. To determine the accuracy coefficient of the model, 2020 LU/LC simulation performed using the transition potential matrix of 2000 and 2010 LU/LC data. The results of simulation were compared the data of land use land cover with the 2020 to evaluate the accuracy of the simulation model. The model of MLP‑ANN provided an accuracy of 72% based on the kappa fit index. According to MLP‑ANN model 2030 results were an increase of 73.33 km² in built up areas, an increase of 56.89 km² in bare areas, and a decrease of 129.66 km² in green areas. It provided a reference basis for future Samsun urban to rural coastline LU planning and management and LU structure optimization.

Urban roads play a significant role for analyzing the complex structure of the city. The climatic features of the city provide information about the comfort of the inhabitants. The climate changes may be observed on a micro-scale due to the increase of impermeable surfaces. Surfaces such as concrete and asphalt contribute to the formation of an urban heat island as they store the temperature. This study aims to determine the effects of plant material, which is a significant element of urban roads, on temperature arising from surface materials. In this context, the effect of Ficus retusa-nitida on the formation of temperature caused by the surface material has been revealed. The study was carried out in Iskenderun, the second largest district of Hatay province in terms of population, in 3 stages in a periold of 6-months between July and December 2020. In the first stage, the studies on the subject were brought together and the points to be measured in the area were determined. At this stage, asphalt (shade-under F.retusa-nitida, sun) and parquet (shade-under F.retusa-nitida, sun) surfaces were selected at a total of 8 points located mutually on the main road axis. The second stage of the study is the stage where surface temperature measurements are made. Measurements were performed once a month at 06:00, 09:00; 12:00, 15:00; 18:00; 21:00 and 24:00 using an infrared thermometer at a height of 150 cm from the surface with 3 repetitions. At the third stage, all data were transferred to GIS using ArcGIS 10.5 software and modelled by using the Kriging Interpolation Method. In line with the results obtained, suggestions for the selection of surface material and the use of plant materials on urban roads have been developed.

Urban roads play a significant role for analyzing the complex structure of the city. The climatic features of the city provide information about the comfort of the inhabitants. The climate changes may be observed on a micro-scale due to the increase of impermeable surfaces. Surfaces such as concrete and asphalt contribute to the formation of an urban heat island as they store the temperature. This study aims to determine the effects of plant material, which is a significant element of urban roads, on temperature arising from surface materials. In this context, the effect of Ficus retusa-nitida on the formation of temperature caused by the surface material has been revealed. The study was carried out in Iskenderun, the second largest district of Hatay province in terms of population, in 3 stages in a periold of 6-months between July and December 2020. In the first stage, the studies on the subject were brought together and the points to be measured in the area were determined. At this stage, asphalt (shade-under F.retusa-nitida, sun) and parquet (shade-under F.retusa-nitida, sun) surfaces were selected at a total of 8 points located mutually on the main road axis. The second stage of the study is the stage where surface temperature measurements are made. Measurements were performed once a month at 06:00, 09:00; 12:00, 15:00; 18:00; 21:00 and 24:00 using an infrared thermometer at a height of 150 cm from the surface with 3 repetitions. At the third stage, all data were transferred to GIS using ArcGIS 10.5 software and modelled by using the Kriging Interpolation Method. In line with the results obtained, suggestions for the selection of surface material and the use of plant materials on urban roads have been developed.

In agricultural production planning; compiling the data correctly, and using and interpreting the data precisely have strategic importance. This study aims, it is aimed to develop a model that can evaluate the suitability of the Lower Kelkit Basin for wheat farming by using the Analytical Hierarchy Process (AHP) and current GIS technologies. In the study, weight values of seven different criteria of topography (slope, aspect) and soil (texture, organic matter (OM), CaCO3, EC, pH) were calculated with AHP. These weight values and standardized criteria maps were combined within the ArcGIS Weighted Overlay tool and the result maps were created according to the FAO suitability index. According to these maps, 54% of the Lower Kelkit Basin was modeled as unsuitable (N) for wheat, 22% as moderately suitable (S2), and 24% as highly suitable (S1). In addition to all these, an editable and updatable ArcGIS model tool was also produced as a result of the study. Our results indicated that AHP and GIS are powerful and effective tools that can be used in land suitability modeling.

The study is aimed at predicting soil erosion and investigate its spatial distribution in Souss basin area used EPM (erosion potential model), also known as Gavrilovic method, incorporating into GIS (geographic information system) software. The spatial distribution of soil erosion shows three main zones in the studied area (very slight, slight to moderate). The main factors in the EPM (soil erodibility, soil protection, slope, temperature and rainfall) were evaluated using GIS software. Data layers used in this study were created from digital elevation model (DEM), lithology maps, landsat 8 oli digital images, the highest amount of erosion occurred in the northeast regions, Results showed that about 87.84% of the study area is classified in low and very low to destructive erosion intensify, 12.15% of the study area was moderate potential soil losses.

Developments in Geographic Information Systems and Remote Sensing (RS) technologies and innovative approaches emerging in deep learning (DL) supported analysis methods have an important place in disaster research as in every field. Convolutional neural networks such as Mask RCNN, U-NET, one of the deep learning methods for disaster damage impact assessment and classification, have started to show successful results. However, high-resolution geospatial imagery and drones provide faster and more accurate detection of structural damage.  In this study, damaged building detection was performed using Göktürk-1 satellite images from 6 February 2023 using Mask-RCNN architecture. In this study, deep learning methods were used to detect the collapsed buildings in the city of Malatya during the 6 February 2023 earthquakes. The study aims to emphasize the significance of GIS and remote sensing for the timely and efficient evaluation of building damage after a disaster. Considering this, high quality images of Malatya city before and after the earthquake were analyzed and data sets were created by masking using Mask RCNN deep learning method through ArcGIS Pro 3.4.0 software. According to the results of the research, it quickly detected damaged buildings with an accuracy rate of 70% according to satellite images after the earthquake. As a result, GIS and deep learning models were used to detect and map the initial damage after the earthquake.

Agricultural land cover has changed over time, and monitoring these changes has become an effective tool in development processes by linking them to ecological and socio-economic issues. In this context, the main hypothesis of the study is that “determining land use changes spatially and temporally using CORINE Land Cover data is crucial in development-oriented planning processes.” The study analyzes changes in agricultural land use based on CORINE land cover classes between 1990 and 2018 in the districts of Saray, Şarköy, and Hayrabolu in Tekirdağ Province, which have low levels of socio-economic development. The research aims to answer the following questions: during which periods did significant changes occur in agricultural areas in Saray, Şarköy, and Hayrabolu? What are the total rates of increase or decrease in these areas? Into what types of land have agricultural are+as significantly transformed? The methodology was developed using Geographic Information Systems (GIS)-based maps, graphs, and tables. The findings reveal the spatial and temporal dimensions of changes in agricultural land use and land cover between 1990 and 2018. The data obtained are anticipated to provide a concrete foundation for developing rural development policies and ensuring the sustainable use of natural resources.

Basin-based water management strategy is one of the necessary instruments for the protection and sustainable use of water resources against climate change. In this paper, the monthly precipitation distributions of the 25 major basins in Türkiye were produced, and amounts and volumes were computed and analyzed. Only annual modeling and assessments of precipitation may hide months with precipitation shortages. Empirical Bayesian Kriging (EBK), Ordinary Kriging (OK), and Inverse Distance Weighting (IDW) were implemented in interpolation. EBK outperformed in all months and calculations were based on the EBK. The month with the highest precipitation potential in Türkiye is December (77.9 mm, 60.77 billion m3), and the month with the lowest precipitation potential is August (13.8 mm, 10.76 billion m3). In the basins, the monthly precipitation amounts range between 2.7 mm and 185.2 mm, and the volumes range between 0.02 billion m3 and 13.24 billion m3. The basins with the highest precipitation depth were determined as the East Black Sea, Antalya, Asi, and Ceyhan, and the lowest as the Small Menderes, Konya, and Tigris-Euphrates in different months. The monthly precipitation patterns and potentials of the basins vary widely. In May, June, July, August, and September, when water, particularly agricultural irrigation, is required the most, the 20 basins, except for the 5 located in Northern Türkiye precipitation shortage was determined.