Assessment of flood susceptibility utilizing remote sensing and geographic information systems: A case study of Mpazi sub-catchment in the city of Kigali
The Mpazi sub-catchment has been facing recurring floods, which pose significant threats to the community and environment. However, GIS technology has proven to be a valuable tool in assessing flood risks and vulnerability in the region. By analyzing spatial data such as land use, elevation, and rainfall patterns, detailed flood maps can be generated to simulate flood scenarios and develop effective management plans. The study conducted in this region revealed that there is a high susceptibility to flood hazards, particularly during the rainy season. The study identified the most vulnerable areas in the region and categorized them as follows: very high risk (39.74%), high risk (13.02%), moderate risk (30.22%), low risk (5.12%), and very low risk (11.9%). It is important to note that floods not only impact the environment but also infrastructure, such as residential and commercial buildings. The insights provided by this study are invaluable for stakeholders in developing effective flood management strategies to mitigate. Hence, all concerned government departments and citizens should collaborate actively to alleviate the ongoing rise in flooding and its impact. Adherence to land use and zoning regulations is crucial in this regard to address the issue effectively.
A geo-spatial analysis of precipitation distribution and its impacts on vegetation in Rwanda
Rwanda is home to a diverse and picturesque landscape that encompasses a range of ecosystems, including rainforests, savannas, and agricultural regions. The intricate relationship between rainfall patterns and vegetation types shapes these varied landscapes, which are crucial for supporting biodiversity and agricultural productivity across the country. Our comprehensive geospatial analysis employs advanced geographical information systems (GIS) techniques and remote sensing data to assess spatial and temporal variations in precipitation across distinct regions of the country. Utilizing historical precipitation data and satellite-derived vegetation indices, our analysis spans extensive periods, incorporating annual and monthly rainfall records from 1990 to 2020 and MODIS/Terra Vegetation Indices spanning 2000 to 2020. Advanced remote sensing methodologies are employed to investigate the correlations between precipitation patterns and vegetation dynamics. The study reveals discernible spatial variations in Rwanda's precipitation distribution, elucidating marked seasonal fluctuations. Identified regions experiencing notable changes in precipitation levels exhibit a direct impact on vegetation health and density. Recorded annual rainfall data illustrates variations across different years, indicating fluctuating levels such as 1160.1 mm (1990), 1078.2 mm (2000), 1402.4 mm (2010), and 1391.1 mm (2020). Corroborating NDVI imagery demonstrates increased vegetation cover in 2010 and 2020, aligning with higher recorded rainfall during these years. The research underscores the significance of these findings in understanding the intricate interplay between precipitation distribution and vegetation dynamics and offers actionable insights essential for sustainable land management, optimized resource allocation, and the formulation of resilience-building strategies. These insights are particularly crucial in the context of adapting to and mitigating the effects of climate change.
Land use and land cover classes affected by the possible sea level rise in Mersin city center (Türkiye)
In this study, a sea level rise (SLR) investigation was carried out in an area representing the Mersin city center located in the south of Türkiye. The study area covers an area of ca. 385 km2. Future projections provided by the Intergovernmental Panel on Climate Change (IPCC) were used for the SLR assessment. These projections are for the years 2100, 2200, 2300, 2400, and 2500 and the SLR for these periods are 0.83 m, 2.03 m, 3.59 m, 5.17 m, and 6.63 m, respectively. It is aimed to determine the areas affected by the SLR that will occur according to these projections. In this context, land use and land cover (LULC) data were obtained from the CORINE 2018 dataset. The data obtained were adapted within the boundaries of the study area and processesed using various GIS analyses. The results have shown that all LULC classes are greatly affected by the SLR, but in varying degrees. Land losses as a result of SLR are as follows: 0.4% at 0.83 m SLR, 9.8% at 2.03 m SLR, 16.7% at 3.59 m SLR, 21.6% at 5.17 m SLR, and 25% at 6.63 m SLR.
Comparative evaluation of the performance of different regression models in land valuation
Lands can play a dominant role in the real estate market, especially due to their legal zoning rights. These properties are preferred investment options compared to financial instruments due to factors such as high returns and long-term reliability. Today, Machine Learning (ML) algorithms are used to accurately determine the land value. Regression models, capable of handling complex relationships, integrating Geographic Information System (GIS), and providing a comparative approach, lead the way among these algorithms. In this study, Lasso, Elastic-Net, ML.Net, and Ordinary Least Squares (OLS) regression models were employed to predict land values in the central neighborhoods of Konya's Selçuklu, Meram, and Karatay districts. The datasets containing legal, physical, spatial, and local criteria of 440 lands were obtained, and GIS analyses were conducted to prepare the spatial data. Based on the modeling results, it can be observed that ML.Net exhibited successful performance with metric values of MAE=0.043, MSE=0.005, RMSE=0.060, and R2=0.82. Comparatively, ML.Net's 9% superior performance compared to the commonly encountered OLS in the literature is of significant importance. The results demonstrated the usability of various regression models for land valuation and highlighted that ML.Net can yield improved outcomes, particularly in modeling high-market-value lands.
Wildfire hazard and risk assessment: The case of Gabala district
As one of the main natural resources for humans, protection of forest resources is one of the main ecological problems of the world. Forests are a source of oxygen, as well as they have some features that can ensure ecological balance. For example, forests are one of the major factors that prevent landslides, erosion processes, flood events, as well as protecting land resources, hydrological resources and optimization micro climatic condition. Decreasing of forest stock affects the fauna directly. There are some factors that impact decreasing of forest resources, for example, settlements, industry and forest supply and etcetera. Forest fires occurring in different parts of the world every year eradicated acres of forest stock. The formation of forest fires is influenced by factors such as climate, anthropogenic and topological effects. Research area is district of Gabala, which is situated at south slopes of Greater Caucasus. Gabala is distinguished by the abundance of forest resources in the territory of Azerbaijan, which is poorly provided with forest reserves. 32405.15 hectares of this district are covered with forests. Taking into account that 40% of the fire incidents that occurred on the southern slope of the Greater Caucasus in 2021 and 2022 took place here, this place was taken as a research area. Wildfire hazard and risk assessment and fire risk zonation, anthropogenic and topological effects are considered in this article and mapping had been done. The resulting values were classified according to the risk group and the results were compared with the fire area data. As a result of the comparison, was not found fire process in the categories of no risk or low risk. 90% of fire incidents could be classified as medium risk, high risk and critic high-risk categories. Consequently, this is an indicator of the validity of the selected parameters and the conducted assessment.
Determining temporal and spatial changes in air quality in the city of Nevşehir
Air pollution, which has notably surged from the Industrial Revolution onwards, has had adverse effects on all forms of life, primarily humans. Factors such as urbanization, intensified industrial activities, growing production and consumption, and the proliferation of vehicular traffic have significantly contributed to the escalating impact of air pollution. This study aimed to evaluate the levels of particulate matter 2.5, particulate matter 10, and carbon dioxide across diverse locations within Nevşehir city center. Sampling was conducted at ten distinct points, utilizing the CEM DT-9880 device for particulate matter and the EXTECH Easyview 80 CO2 Analyzer device for carbon dioxide. Measurements were taken during both morning and evening hours to discern daily fluctuations, as well as in both summer and winter to identify seasonal variations. Data collected were input into QGIS software and interpolated to delineate the distribution of pollution throughout the city. The findings reveal that pollution levels are significantly elevated across the city, particularly in industrial zones, organized industrial districts, and densely populated residential regions, as well as in areas where coal is extensively utilized for heating. Conversely, parks and green spaces exhibit the lowest levels of particulate matter and carbon dioxide pollution. While complete eradication of pollutants may be unattainable, it is crucial to mitigate pollutant levels to ensure smooth urban living. To achieve this, installing filters on industrial chimneys, expanding natural gas infrastructure, and promoting the use of natural gas in non-infrastructure areas is essential. Should coal be employed for heating, selecting high-quality, low-soot Siberian coals with high energy content is advisable. Enhancing the city's green areas and parks, as well as expanding afforestation initiatives, holds merit. Efforts to curtail vehicular numbers, encourage public transportation, and prioritize carbon emission-free electric vehicles are crucial for fostering a cleaner urban environment.
Spatial ecological risk analysis in peach farming in Manisa
With the help of information technologies, which are developing day by day, it has become easier to perform agricultural analyzes. Positional analyses can be performed with the help of Geographical Information Systems by gathering Climate, Soil, Topography and İrrigation data related to Agriculture. These analyses enables to generate analyses for agricultural investment maps, areas of agricultural conformity, plant pattern determination, etc. The purpose of this study is to prepare "Product Based Agricultural Risk Analysis Maps". Climate, Soil, Topography and Irrigation data, which are important in the growing of agricultural products are collected, severity and prospects for risk analysis are determined separately and risk values are established for each risk factor. The total risk value was calculated by prioritizing risk factors using the Analytical Hierarchy Process (AHP), one of the multi-criteria decision-making methods. Thanks to AHP, a methodology for calculating scenario-based risk values has been developed taking into account different probabilities. With the developed model, risk maps were created for climate, soil, topography and water constraints. The total risk map was obtained by combining the risk maps created with AHP. In this study, a model was established by taking the Peach and Fig product of Manisa and as a result of the model, Total Risk values were divided into classes such as "High Risk Areas", "Medium Risk Areas", "Low Risk Areas" and "Strictly Not Recommended Areas" according to the scores they received positional.
A post earthquake damage assessment using GIS in district Mirpur, Pakistan
A moderate earthquake of magnitude Mw 5.6 struck the Kashmir region of Pakistan on September 24th, 2019, causing severe damage to buildings and infrastructure. The study aims to assess the extent of damage in Mirpur district, Azad Kashmir. In this research, the map of the affected area and damages is developed using Geographic Information System (GIS). Results indicated that damage to roads and houses is severe in the proximity of Mangla Dam. It was found from the analysis that shallow earthquakes with high magnitude can have disastrous consequences. It also poses the risk of Dam Lake Outburst Flooding (DLOF) which can cause disaster in the downstream regions. So, there is the utmost need to work on the mapping and hotspot analysis of the places susceptible to earthquakes. The result and findings of such a study can assist disaster management authorities and donor agencies reduce the risk of potential damage and suffering by preparing earthquake risk reduction plans and investments in disaster preparedness and prevention.
Assessing hydrological modeling approaches: a review of the soil conservation service curve number and the soil and water assessment tool
This article reviews two hydrological modeling tools, the Soil Conservation Service Curve Number (SCS-CN) model and the Soil and Water Assessment Tool (SWAT), and the Analytic Hierarchy Process (AHP) method used for estimating surface runoff and evaluating the impacts of land use changes on watershed responses. The SCS-CN model has been widely used for estimating surface runoff from rainfall, and its integration with GIS and remote sensing has improved its accuracy and precision. The SWAT model has also been effective in assessing the impact of land cover and land use changes on hydrologic response. The AHP method has been used to suggest the best locations for rainfall water harvesting in arid regions. However, these models also have limitations that should be considered when applying them to different watersheds. Proper calibration and validation of the models' input parameters are crucial to ensure accurate results, and the models' performance can be affected by uncertainties in the input data and model parameterization. Despite these limitations, these tools remain useful for evaluating surface runoff and its impact on water resource management, flood control, erosion prevention, and sustainable land and water management practices. In conclusion, the SCS-CN model, SWAT model, and AHP method are important approaches to evaluate surface runoff and its impacts, but their limitations and suitability for different watersheds should be carefully considered.
Determination of densities of existing intersection by buffer analysis (Samsun- Atakum intersections)
This study was conducted to evaluate the traffic density of nine intersections located between the Highways intersection and the Toplu Konut Boulevard intersection on Atatürk Boulevard in Samsun's Atakum district, and to analyze the traffic flow in detail at these intersections. Analyzes were applied with ArcGIS 10.3 from GIS software. In the study, kernel density analysis was used to determine the densely populated areas, and buffer analysis was used to determine the density levels of intersections. Kernel density analysis was used to determine the density of settlements, then density maps were created depending on the buffer zones created by schools, institutions, hospitals and densely populated areas around the intersections. Buffer analysis was attempted at buffer sizes of 500 and 1000 meters and mapped in GIS software. As a result, it has been determined that the intersection with the least density is Vatan street intersection, and the ones with the highest density are YEDAŞ and Türk iş intersection. Thus, a local problem was identified and solution suggestions were presented.
Applying an integrated approach to evaluating ecotourism capabilities in Margavar district, northwest Iran
Ecotourism considered as purposeful travel to the natural and cultural history of nature to understand the environment by avoiding change Ecosystems, environmental degradation, and creating economic opportunities. Moreover, this is one of the new sources of social activities in the field of sustainable development. Our study aims to assess and mapping the ecotourism capabilities of the Margavar rural district, in northwest Iran. In this regard, a set of associated factors were considered. Hence, we want to evaluate the ecotourism capabilities through an integrated approach using the Analytic Network Process (ANP) method, VIKOR technique, Geographic Information System (GIS), and considering the environmental and tourism criteria. A Fuzzy gamma operator was used to overlay the weighted maps and the ecotourism map was obtained. The overall results indicated that 14.50% of the study area is located in a very high potential class, 26.32% in a high potential class, 32% in a moderate potential class, 18% in a low potential class, and 9.18% in a Very low potential class. Due to the pristine nature of the study area and the need to create new investment opportunities, our findings can be useful in identifying the ecotourism potentials of the region and achieving a higher level of local development.
Evolution and future trends in global research on geographic information system (GIS): a bibliometric analysis
In this article, the global research status and future trends of GIS are examined using bibliometric analysis. This study is based on an analysis of the most cited articles on GIS research topics, scientific production, collaboration between countries and authors, and GIS research from the Scopus database from 1969 to 2023. A total of 6398 articles were published in 3983 different journals, most of them original articles (4040, 63.1%) and conference proceedings (1701, 26.6%). The United States is the most productive country with 1847 articles and 29% of the total literature, followed by China with 596 articles and 9.3% of the total literature. Chinese Academy of Sciences was found to publish the highest number of documents with 72 articles and to cover 1.1% of the total literature followed by United States Geological Survey with 42 articles and to cover 0.6% of the total literature. Most used six keywords were “Remote sensing”, “Spatial analysis”, ‘‘Mapping’’, ‘‘Groundwater’’, ‘’AHP’’ and ‘‘Big data’’ (n = 255, 164, 130, 121, 98, and 97 times, respectively. The top two authors who contributed most were Goodchild, M.F. (22 articles), Ehlers, M. (14 articles). The top-cited publication was ‘‘Extracting topographic structure from digital elevation data for geographic information system analysis’’ with 1964 citations.
Landslide susceptibility analysis with multi criteria decision methods; a case study of Taşova
Natural or artificial based disasters threaten to humans. In order to minimize the loss of life and property that may occur after a disaster, various studies are carried out. One of these studies is disaster risk maps. To produce disaster risk maps, the criteria which affecting the disasters should be determined. In order to evaluate more than one criterion Multi-Criteria Decision Making Methods (MCDM) and Geographical Information Systems (GIS) are used. MCDM methods are used both to weight criteria and to rank among alternatives. In the current study Taşova district of Amasya province is used as study area and two different Landslide Susceptibility Maps were produced based on Analytical Hierarchy Process (AHP) and Full Consistency Method (FUCOM) for this region. A total of twelve criteria were determined for production of risk map and raster data was produced by performing various spatial analyzes for the current criteria. Two different landslide susceptibility maps were obtained by giving criterion weights to the generated raster data. It was observed that, Risk-free area, low risk area and high risk area rates are almost equal, but medium risk area and risk area rates are different in two different weighting methods.
Risk assessment of Rawal dam outburst flood using integrated hydrological and geo-spatial approaches
This study is an effort of Rawal Dam outburst flood risk assessment using integrated hydrological and geo-spatial approaches. Satellite data sets including Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) and Landsat-8 image having 30 m spatial resolution were downloaded from United State Geological Survey (USGS) geo-database. Watershed modeling was applied to delineate the catchment area and drainage geo-visualize network. Spatial hydrological model was utilized to the vertical and horizontal profile of estimated flood in the downstream areas in case of dam breaching. Similarly, Google Earth Pro was used to calculate lake volume. The estimated flood has depth of more than 15 m. Downstream areas are at high risk. The results of the study can assist disaster management authorities and decision-makers in devising location specific effective flood risk reduction strategies in the region.
Identifying impervious surfaces for rainwater harvesting feasibility using unmanned aerial vehicle imagery and machine learning classification
As global demand for clean, reliable water sources continues to increase amid a growing population and the impacts of climate change, effective water conservation and collection methods are more important than ever. Rainwater harvesting has long been a reliable technique for capturing and preserving water, and recent advances in geographic information systems (GIS), unmanned aerial vehicle (UAV) remote sensing, and machine learning (ML) image classification have significantly improved our ability to accurately assess the potential for rainwater harvesting. This study leveraged these technologies to evaluate the feasibility of rainwater harvesting at the Osmanbey Campus of Harran University, using UAV imagery and ML classification techniques to identify suitable surfaces for collection. The results showed that it is possible to irrigate a grass area of 4417 square meters daily for a year using the potential harvested rainwater in the study area, demonstrating the significant potential of rainwater harvesting as a sustainable water source for irrigation purposes.
Valuation of commercial real estate in Ankara middle east industry and trade center (MEITC)
Commercial real estate is an important part of the real estate sector. In the commercial real estate market, value estimates are made with the help of the characteristics of the samples that are subject to purchase and sale, and they are used in transactions such as taxation, insurance and privatization. In this study, collective valuation processes are aimed by applying the Multiple Regression Analysis (MRA) statistical method to the commercial properties offered for sale in the Middle East Trade and Industry Center (MEITC) in Yenimahalle district of Ankara. In this direction, 31 office sales data were collected for valuation, 29 criteria affecting the value were determined, and a mathematical model was developed according to independent variables. R², Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percent Error (MAPE) were considered. Their results were found as 0.938; 0.00; 0.04; 0.42 respectively. Based on these values, it has been concluded that the regression method, which is seen to produce high accuracy, can be used in commercial real estate valuation. Geostatistical analyzes were made using market values and estimated values, and value maps were produced in the Geographical Information Systems (GIS) environment.
Spatial assessment of forest cover change in Azad Kashmir, Pakistan
In this study spatial assessment of forest cover in Azad Kashmir was carried out. The study is based on secondary data. Satellite imageries of the past 20 years having temporal extent of five years were utilized for assessment of changes in vegetation covers. Supervised image classification and Normalized Difference Vegetation Index (NDVI) techniques were used to achieve objective of the study. The results indicated that forest cover is declining leading to different environmental problems. Increase in population acted as booster for the factors such as deforestation, conversion of forest land into agriculture and built-up land. These anthropogenic activities put pressure on the forest of the study region. After 2005 disastrous earthquake, rapid development of built-up environment was the leading factor behind clearing of forest. It was found from the analysis that forest cover has significantly declined in the last two decades and affected ecosystem services. Location specific management strategies are highly recommended to reduce the rapid decline in forest cover. Strategy must also consider the population growth and traditional forest conservation strategies. Alongside, keeping in view the agricultural activities and infrastructural development in the dense forest cover areas are also recommended.
Spatio-temporal analysis and trend prediction of land cover changes using markov chain model in Islamabad, Pakistan
Rapid urbanization is changing the landscapes of urban areas and affecting the quality of life and environment. One of the most dynamic components of urban environment is land cover, which have been changing remarkably since after the industrial revolution at various scales and population growth. Frequent monitoring and land cover change detection provides a better understanding of functions and health of urban environment. Remote sensing and Geographical Information System (GIS) are advanced techniques to visualize these dynamics in the digital map. Therefore, this study aims to analyze the existing spatial extent of different land cover classes and predict the future trend in Islamabad; Capital city of Pakistan, by applying Cellular Automata (CA)-Markov model. For this aim, three consecutive-year Landsat imagery (i.e. 200, 2010, 2020) were classified using the Maximum Likelihood Classifier. From the classification, three LULC maps with four class (Barren Land, Vegetation, Water Body, built up were generated, and then change-detection analysis was executed. Using remote sensing data, we simulated Spatio-temporal dynamics of land use and land cover changes Simulation results reveal that the landscape of Islamabad city has changed considerably during the study period and the change trend is predicted to continue into 2030. The study observed a significant increase in built-up area from 2000 (9.53%) to 2020 (28.2%), followed by an increase in the cover of bare ground. On the contrary, vegetation cover declined drastically 2000 (28.61%) to 2020 (25.08%). Rapid population growth triggered by rural urban migration coupled with hasty socio-economic development post democracy are the main drivers of these changes. Under the business as usual scenario, prediction analysis for the year 2025 and 2030 show that built up area will consume almost all of the city area (47.04%) to (57.25%) with vegetation significantly reduced to patches making up only about (17.23%) to (14.4%) of the city. These findings demand for an urgent and effective planning strategies to protect the existing vegetation covers, agricultural land, and limit the growth of built-up land. The study has also potential in planning sustainable cities.
Heat maps of U-Th enrichments in open source coded geographical information systems (GIS); Arıklı (Çanakkale, Turkey) district
Uranium (U) and Thorium (Th) are both strategically important elements in the world. For this reason, it is exceedingly significant to explore for these elements, which are enriched in many geological environments. U-Th enrichments in Turkey were usually discovered in the Western Anatolian geography. U-Th anomalies were still observed in the Arıklı region in the south of Çanakkale. Remote sensing (RS) and geographic information systems (GIS) are platforms for the map-based evaluation of geological structures. In this study, RS and GIS were utilized in the QGIS program to carry out the geochemical data obtained in the field more understandable. GIS and remote sensing methods have been widely used to analyse the formation, origins, and processes of geochemical characters in ore deposits. U-Th values, which show high values in the fault zones in the Arıklı region, are immediately related to the CaO values. As in many geological studies, remote sensing and GIS studies suggest the opportunity to check out field data with a significant quality in the exploration of ore deposits.
Geochemical heat maps in complex geological structures via using QGIS: Maden (Elazığ) district
Geochemical analysis results are one of the most significant indicators that reveal the characteristics of the geological structures in a region. In particular, the differences in the composition of complex geological structures can be evident in field and Geographic Information Systems (GIS) studies. Turkey includes a character consisting of quite complex features with its geological structure. The Maden (Elazığ) complex has also attracted the attention of many researchers with its complex structure. The Maden Complex also contains basalts, basaltic andesite, andesite, dacite, diabase, and pyroclastic rocks, which are intercalated and lateral-vertical transitive with all these sedimentary successions. Mapping and lithological differentiation of the Maden Complex, which has a very complex structure, is difficult. The geochemical analysis of the samples taken from the field was transferred to the GIS environment with their coordinates. Thematic maps are created to make the geological interpretations in this region cleaner and the field data more predictable. These maps also allow the correlation of major oxides and trace elements. In this study, the geochemical data obtained in the Maden Complex were analyzed in the QGIS program. The geochemistry of the region has been made more understandable and interpreted with heat maps. The diversification of thematic maps, which gives a new perspective to geochemical data, will provide more support to geological studies.
A geographical information systems (GIS) perspective on European green deal and sustainability
The European Union (EU), known for its sensitivity to environmental and social sustainability issues, especially on topics such as combating climate change, reducing greenhouse gas emissions, and using renewable energy since the 1990s, took these sensitivities one step further in November 2019. In November 2019, it took these sensitivities one step further and presented a package of initiatives, a commitment by the Union to take firm and ambitious steps in environmental and sustainability issues: The European Green Deal. The Memorandum aroused significant repercussions for all state, international organisations and private sector players that have economic, political and geographical connections with the EU, because the Consensus, which consists of the standards set by the EU for its member states, also has the potential to affect the relations of EU countries with third parties. This situation has revealed the necessity of a good understanding of the Agreement by everyone, given the wide commercial and diplomatic ties of the EU. In the geographic data infrastructure designed within the scope of this study, a total of 26 intermediate criteria grouped in 5 main criteria were used. Within the scope of the study, we aim to test the analysis to be made with the availability of quantitative data and topographic data by the European Green Reconciliation. By making use of these data used in the continuation of our study, expert opinions, surveys, etc. can be weighted with AHP or TOPSIS method, and positions, companies, etc. that are important for the Green Reconciliation can be determined.
Regression analysis and use of artificial neural networks in housing valuation forecasting: case example of Güvenevler neighbourhood in Mersin
Real estate valuation is the process of determining the value of the real estate made to evaluate the factors related to the properties, use and benefits of the real estate. Along with the developing technologies, real estate valuation processes move to modern evaluation methods. In this study, forecasting models were developed for sale price using Regression and Artificial Neural Networks (ANN) methods by using residential data for sale located Güvenevler neighbourhood in Mersin. In this study, the effects of some physical properties such as age, location, number of rooms, balcony condition, number of bathrooms, gross area, facade of the houses in Güvenevler neighbourhood on sales pricing were investigated. In this study in order to make the data more meaningful, Min –Max normalization was performed and regression and ANN modelling was done. SPSS program was used for regression analysis and Matlab program was used for ANN method. With the developed forecasting model, it is aimed to control the price tracking of the old residential district Güvenevler neighbourhood. According to the results of the study, while the rate of explaining the total variation of the independent variables in the housing prices is 92% in the regression analysis, in the network created with ANN, the ratio of independent variables to explain dependent variables is 93%. Both methods are successful because they give results within certain limits.
Researching the use of infrastructure in land management
Since soil is the cornerstone of human life, it has an important place in both individual and social life. Therefore, the soil needs management for its continuity, that is, its sustainability. Infrastructure is the general name of the facilities such as transportation, electricity, internet, drinking water, sewerage, road and landscape for a building or an area where people live, work or reside. Infrastructure work and facilities are of great importance for the quality of life and economy. In this study, the use of infrastructure in land management was investigated. The importance and contributions of land management were researched and presented during the infrastructure project design and implementation studies. Various problems can be encountered while making infrastructure projects. If we reduce these problems to 3; we can reduce the passage of highways as passing from the state waterworks structure and passing from the private property. In the study, although we talk about how these 3 problems can be solved in general, it has also been tried to find solutions to other problems and problems. The technical specifications of Iller Bank, which has become professional in infrastructure in our country and have carried out many studies with its technical team, have been used as material.
Automatic construction of a knowledge base for transport networks
Abundant spatial transport data is accessible from governmental institutions or internet sources contributed from all over the world. This kind of data obtained from various sources exists in different structures and prevents interoperability. The aim of the research, in this paper, is to develop an ontology compatible with specifications of transport networks of Infrastructure for Spatial Information in the European Community (INSPIRE) and to obtain a knowledge base of transportation data from non-semantic data sources automatically. In the first stage, ontological classes and relations in the transport network are explained. Then, defining algorithms in three main phases, non-semantic spatial data is transformed into the ontological structure. Some ontological queries are implemented to retrieve information from a knowledge base that is produced because of the transformation algorithms. As a result of the study, the transportation datasets in the relational database have been successfully converted into a semantic data format. The functionality of the obtained knowledge base was validated with queries that allow semantic reasoning.
Geospatial intelligence (GeoINT) risk maps producing with geographic information systems (GIS) and creation of the 2D simulation model
In today's world, many complex and hybrid warfare models are applied. Operational action plans play an active role in the implementation of these models. These plans use intelligence and spatial data as a base. At this point, we come across the concept of spatial intelligence, which brings together space and intelligence. Spatial intelligence; it is the analysis of data related to location, space and geography by bringing together data. In this study, the concept of intelligence and geospatial intelligence will be explained in detail. Afterwards, risk studies from the literature will be included. Finally, the modeling of risk in GIS environment and the production of risk maps will be discussed from a geospatial intelligence perspective. By transferring the steps of a model application with the aim of contributing to the literature of the study. The geospatial intelligence of the risk will be revealed with a 2 dimension three simulation model.
Determination of unsuitability points on the route of Van Gölü-Kapıköy railway line by using GIS and AHP method
It is seen that there is a parallelism between the economic power of the countries and the development of the logistics sector of their sustainable competition. According to the free market economy in the Keynesian economic system, the logistics sector has an important function. With the globalization of the world, the logistics sector gains importance and this makes railway logistics gain importance. The development of railways allows the development of the economy. Since it positively affects the cost-benefit analysis of public institutions and organizations and the private sector, the railway route should be chosen in the most appropriate way. In the determination of the route Geographic Information System (GIS) applications are used with the developing technology in recent years. Geographic Information Systems applications, on the other hand, play a major role in collecting data analytically, obtaining results and using the results thanks to computer technologies and software systems. The Analytical Hierarchy Process (AHP) method, which is used together with Geographic Information System (GIS) applications, makes it possible to provide the most appropriate and maximum benefit in line with certain parameters by comparing the alternatives in the decision-making process in the most appropriate way. These parameters considered include slope, aspect, stream, building, small water flow and transportation. Taking these parameters into account, the conditions affecting the railway line were evaluated using the AHP method.
WebGIS allows everybody to reach geo-spatial data. It creates a rapid and advanced environment without place, time and the limitations of high processing power and high client computer. WebGIS, reshapes and makes use of all computer functions including gathering attribute data, storing, acquiring, analyzing and visualizing. WebGIS was employing the internet based maps and client-server architecture in order to provide these facilities to all. This architecture, however, has been switched to service oriented architecture, not being able to meet cope with, increasing data volume and number of access requests. Service oriented architecture, provides a service system, dynamic, elastic and re-contractible where meeting with different users. Also this improves the decision making progresses by spreading spatial information. This article analyzes WebGIS architectures and Technologies by emphasizing architecture.
Trend analysis of temperature and precipitation in Mediterranean region
Hydrological parameters are heavily affected by climate changes and human activities. To confirm and take actions against such affects and changes caused by these affects trend analyzes must be performed. In this study, Linear Trend and Modified Mann-Kendall trend analyzes were performed using the annual average temperature data (̊C) and the annual total precipitation data (mm) between 1960-2020 of Burdur, Isparta, Kahramanmaraş, Muğla, Antalya, Mersin, Adana, Osmaniye, Antakya stations. The study concluded in increasing trends in temperature data (8 out of 9 stations) and no trend in precipitation data.
Determination of precipitation trend by time series: A case study Erbaa plain
Changes in precipitation occur due to global or local climate changes. Studying this change is very important for human life. Rainfall is very important in meeting essential needs such as agricultural activities and clean water resources. Therefore, trend analysis in precipitation data is important. In this study, in order to examine whether there is a trend in the precipitation data of Erbaa Plain (Turkey), first homogeneity test was performed and then the standard precipitation index was calculated. The calculated data were analyzed using the Mann-Kendall test and Sen's Slope test. Monthly precipitation data for 40 years covering the years 1981-2020 were used in the study. Precipitation data were analyzed according to 90% confidence interval. Trends were detected in January and September in monthly precipitation series.
Landslide susceptibility mapping of Tokat (Turkey) province using weight of evidence and random forest
Landslides are one of the important disasters that have negative effects on people. In this study, the Landslide Susceptibility Map of Tokat (Turkey) province was produced. Slope classes, elevation classes, land use classes, geology classes, aspect classes and proximity to fault lines classes were used during the study. The Weight of Evidence method was applied to determine the relationship between the classes of the parameters and the landslide events. Random Forest method was used to determine the weights between parameters. Weighted Overlay operation was applied to the classified and weighted map data using ArcGIS program. As a result of the process, the data were divided into 5 classes and the Landslide Susceptibility Map was produced. When susceptibility classes are examined, it was seen that 92,42% of the old landslide events occurred in high and very high classes.