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  • Küçük Resim Yok
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    Monitoring of Groundwater Level Change with GeographicalInformation System (GIS), The Case of Konya Altınekin Basin
    (2021) Durduran, Süleyman; Bozdağ, Aslı; Okka, Cafer Tayyar; Alkan, Tansu
    Among the groundwater resources in Turkey, there are quite important basins with their agricultural land use characteristics and ecological characteristics. One of them, Konya Closed Basin (KCB), along with its sub-basins, has Turkey's grain reserves. However, over the last years, over-exploitation of groundwater resources, water-level reductions have become a threat to the ecological structure of the sinkholes and the basin. In order for this process to be analysed with all its causes and measures can be taken, it is necessary to monitor and record the changes that are experienced. In this context, Geographical Information System (GIS) is important as a system that can demonstrate the change in the basin. In this study, the change of groundwater level was investigated with the help of GIS, in order to protect the ecological balance in the KCB Altınekin Basin, where significant decreases in groundwater level have been experienced in recent years. The change in the data of 2015 and 2016, which was obtained from 10 water wells where the groundwater level in this basin was identified, was analysed with the aid of GIS. It has been found that the groundwater level in the basin changes with agricultural land use characteristics, daily use, and climatic changes.
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    Türkiye’de Tarım Arazilerinin Değerlemesine İlişkin Genel Bir Değerlendirme
    (2024) Alkan, Tansu; Durduran, Süleyman Savaş
    Taşınmaz değerleme bir taşınmaza ilişkin nitelik, fayda, çevre, kullanım koşulları gibi faktörlerin dikkate alınarak objektif ve tarafsız bir şekilde değerin tespit edilmesi işlemidir. Taşınmaz değerlemeye arsa ve arazi düzenlemesi, kamulaştırma, toplulaştırma, vergilendirme gibi kamusal uygulamalar ile kredilendirme, sigortacılık, alım-satım gibi bireysel uygulamalarda ihtiyaç duyulmaktadır. Değerlemeye konu olan taşınmazı bulunduğu konuma göre kentsel ve kırsal olarak nitelendirmek mümkündür. Kırsal alanlarda tarım arazilerinin değerlemesi güncel bir konudur. Sürdürülebilir bir tarım arazisi değerlemesi için dinamik bir piyasanın da oluşması önemlidir. Farklı nedenlerle tarım arazilerinin değeri belirlenmekte ve bu değer malik ve ilgili kurumlar için önem arz etmektedir. Bu bağlamda değerlemenin objektif olması için değeri etkileyen faktörlerin matematiksel olarak ifade edilmesi gerekir. Bu çalışmanın amacı, Türkiye’de tarım arazilerinin değerlemesi ile ilgili yapılan çalışmaları incelemektir. Bu kapsamda tarım arazilerinin değerlemesi, kapitalizasyon oranının hesaplanması ve meyve bahçelerinin değerlemesi ile ilgili yapılan çalışmalar ele alınmıştır. Ayrıca tarım arazilerinin değerini etkileyen faktörler incelenerek gruplandırılmıştır.
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    Using GIS-supported MCDA method for appropriate site selection of parking lots: The case study of the city of Tetovo, North Macedonia
    (Selcuk Univ Press, 2024) Jonuzi, Edmond; Alkan, Tansu; Durduran, Suleyman Savas; Selvi, Huseyin Zahit
    The provision of adequate parking spaces for vehicles has emerged as a prominent and challenging issue confronted by towns, cities, and municipal authorities in recent years. Addressing this problem necessitates a thorough examination of the prevailing physical conditions in existing parking areas, while simultaneously undertaking analyses to identify suitable locations for new parking areas or parking lots. This study focuses on the city of Tetovo, North Macedonia, investigating and assessing the available parking areas while analyzing potential sites in accordance with the city's needs and requirements. To facilitate decision-making, a Multi-Criteria Decision Analysis (MCDA) approach is employed to address the parking site selection analysis problem. The weightage of criteria utilized in the analysis is estimated, and potential parking solutions or site selections for new parking areas are identified through the combined application of Geographical Information System (GIS) and Analytic Hierarchy Process (AHP) techniques, identifying primary and sub-criteria, with a focus on Land Use and Transportation as the main criteria for selecting parking lots. The integration of GIS and AHP offers an effective and optimal methodology for site selection and identifying suitable parking locations. AHP method, applied to criteria, determined relative weights through expert opinions, while GIS facilitated spatial analysis for identifying suitable parking locations. The study identifies accessibility to main roads as the criterion carrying the greatest weight (0.517), while accessibility to cultural facilities holds the lowest weight (0.117). The study serves as a pivotal resource for sustainable urban management and decision-making, providing insights into future urban planning and the identification of suitable parking lot sites to foster sustainable development within the city.
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    Using machine learning algorithms for predicting real estate values in tourism centers
    (Springer, 2023) Alkan, Tansu; Dokuz, Yesim; Ecemis, Alper; Bozdas, Asli; Durduran, S. Savas
    Along with the development of technology in recent years, artificial intelligence (machine learning) techniques that perform operations, such as learning, classification, association, optimization, and prediction, have started to be used on data on real estate according to the criteria affecting the value. Using artificial intelligence (machine learning) techniques, valuation processes are performed objectively and scientifically. In this study, machine learning techniques were employed to balance the real estate market, affected by the tourism sector in Alanya district of Antalya province, Turkey, and examine changes in value objectively and scientifically. First, the criteria affecting the real estate value were determined as structural and spatial, and data on real estate were obtained from the online real estate website. Then, the values of the real estate in the selected application area were predicted using machine learning algorithms (k-nearest neighbors, random forest, and support vector machines). Unlike studies in the literature, algorithm-based valuation using machine learning algorithms was performed instead of mathematical modeling. When analyzed for performance metrics, the best result was achieved with the support vector machines algorithm (0.73). Objective methods should be used to balance the exorbitant differences between real estate values, to regulate market conditions and to carry out a real estate valuation process free from speculative effects in coastal areas where tourism factor is effective. This study indicated the applicability of algorithm-based machine learning techniques in real estate valuation.