Akar, Ali Utku
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Akar, A. Utku
Akar, A.U.
Akar, A. U.
Akar, Ali U.
Akar, A.U.
Akar, A. U.
Akar, Ali U.
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auakar@ktun.edu.tr
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02.08. Department of Geomatic Engineering
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Article Citation - WoS: 1Cobb-Douglas Hybrid Modelling Approach With Fuzzy-Ahp Indexing for Residential Land Value Determining: a Case Study of Konya/Turkey(Konya Technical Univ, Fac Architecture & Design, 2022) Yalpır, Şükran; Şişman, Süleyman; Akar, Ali UtkuIn this study, for mass real estate appraisal forecasting, the hybrid mathematical model has been developed by combining Cobb-Douglas one of the nonlinear regression models, and linear modeling. The real estate attributes that create the model were grouped under four main-title: local, spatial, physical and legal features. While Cobb-Douglas was used for the value forecast based on the real estate attributes in each part of the model, an integrated model was created with a linear approach. As a different approach, local and spatial features, which are among the real estate attributes, were used as indexes for reasons such as preventing data confusion in the model and using according to the spatial analysis results of distances. Local and spatial index were prepared with the Fuzzy Analytic Hierarchy Process (FAHP) method to use within the model. For indexes, in the central districts of Konya, 10 local-specific attributes were used, while 12 spatial-specific attributes. The data set has been prepared using legal and physical attributes with market values collected from 457 parcels in the study area. Local and spatial attributes were added as indexes to the data set used in the hybrid model. In addition, modeling was done with the data set used in the Cobb-Douglas Hybrid Model (C-DHM) according to the Linear Multiple Regression Analysis (Linear MRA) method. The developed C-DHM's results was integrated with Geographical Information Systems (GIS). The performance values between the hybrid model and market values were examined. Results showed that R2 value for C-DHM and Linear MRA used as indexes was found to be 0,85 and 0,80. When the values obtained from C-DHM and market value are compared, it is seen that model gives successful results.Conference Object Citation - Scopus: 1Hedonic Modeling of Housing Purchase/Sale Density With Urban Change Factors(International Society for Photogrammetry and Remote Sensing, 2021) Pirbudak, H.E.; Yalpır, A.; Akar, Ali UtkuDue to the industrialization in the cities, land needs have appeared in the increasing urban population. These needs have created houses with the accumulate of collective living spaces in the city. It is necessary to determine the supply-demand relationship and value of these real estates with economic importance for smart urban management systems and decision support systems in the market. The value of real estate varies according to the country in which it is located, but in general, it is affected by many factors such as spatial attributes, demographic factors, building factors, economic conditions. Depending on these factors, values and purchase-sale densities of housing also change.
In this study, for prediction of housing purchase-sale density, hedonic modeling was realized with 15 features from urban change factors. Urban change factors that affect the purchase/sale of housing such as land use, demographic factors, population density and structural factors have been examined through Geographic Information System (GIS). The hedonic regression method was used for predicting the density of housing purchase/sale. As a result of the modeling, it was found as R2?Combining double low line?0,85.
© Author(s) 2021. CC BY 4.0 License.Article Citation - WoS: 11Citation - Scopus: 13Prediction of Zenith Tropospheric Delay in Gnss Observations Using Support Vector Regression(Elsevier Sci Ltd, 2023) Akar, Ali Utku; İnal, CevatModelling of tropospheric delay has a crucial place in the Global Navigation Satellite System (GNSS) as well as atmospheric and space research. Until now, many different modelling put forward and are still being developed to predict tropospheric delay. Developments in Machine Learning (ML) provide alternative approaches to the predictions of Zenith Tropospheric Delay (ZTD) in GNSS observations and allow an increase in the efficiency of current models. This study focusses on Support Vector Regression (SVR) modelling for predicting ZTDs over selected NYAL (North Europe), BAIE (North America), GOPE (Central Europe) and NKLG (Central Africa) stations in different regions globally. The datasets for the SVR are meteorological data, station coordinates (u, k and h) and the site-wise ZTDs obtained from the VMF1 product for the period 2019-2020. SVR model predictions were realized by using Linear, Polynomial and Radial Basis Function (RBF). Predictive results of SVR models were compared through various performance metrics such as coefficient of determination (R2), Root Mean Squared Error (RMSE), etc. The results from the NYAL station show a good level of prediction capability of the RBF-SVR model with average RMSE and R2 of 17.5 mm and 0.859. This model also presents good predictions at BAIE and GOPE stations with average RMSEs of 20.1 mm and 20.3 mm, and R2 of 0.810 and 0.805 respectively. The station with the lowest model success is NKLG with 24.8 mm average RMSE and 0.698 R2. According to these results, it was obvious that the RBF-SVR model achieved more success in mid-high latitudes and the height differences at the stations do not affect the model. In addition, the RBF-SVR model has obtained close and realistic results that are compatible with the IGS-ZTD product. These conclusions indicated that the ML model is usable as a means of improving the data missing in the current ZTD products and predicting daily tropospheric delay. (c) 2023 COSPAR. Published by Elsevier B.V. All rights reserved.Master Thesis Gnss Gözlemlerindeki Troposferik Gecikmenin Destek Vektör Makineleri Algoritması ile Kestirimi(Konya Teknik Üniversitesi, 2021) Akar, Ali Utku; İnal, CevatTroposferik gecikme, GNSS sinyallerinin troposferdeki gaz kütleleriyle etkileşimi sonucu doğrusal hareket etmemesi ve buna bağlı olarak sinyallerin beklenilen sürede alıcıya ulaşmamasından oluşmaktadır. Bu yönüyle troposferik gecikmenin erişilebilirliği ve hassas modellemesi, GNSS konumlandırma uygulamalarının yanı sıra meteorolojik çalışmalar ve hava durumu tahminlerinde önemli bir rol oynamaktadır. Günümüzde GNSS tekniği ile zenit troposferik gecikme etkisi belirlenirken, ani hava değişimine duyarsız, standartlaştırılmış ve deneysel yöntemlere dayalı parametreler içeren troposfer modelleri kullanılmaktadır. Söz konusu modeller gecikme etkisini, meteorolojik sensörü olan GNSS istasyonlarını referans alarak, diğer noktaları enterpole yöntemlerinin ya da deneysel denklemlerin kullanımı sonucu belirlemektedir. Dolayısıyla bu modeller ile gün içerisinde her zamana ait gecikme etkisinin hassas bir şekilde belirlenmesi veya erişilmesi mümkün değildir. Ayrıca teknik veya donanımsal sorunlar nedeniyle GNSS istasyonuna erişilemezse, veri arşivinde meydana gelen boşluklardan kaynaklı gecikme tahmininin kalitesi bozulmaktadır. Bu amaç doğrultusunda, ani hava değişimlerinden kaynaklanan troposferik gecikme etkisinin anlık, sürekli ve doğru bir şekilde belirlenmesi, kestirimde referans istasyonlarının birbirinden bağımsız hale getirilmesi veya teknik/donanım sorunlarının önlenmesi için yeni alternatif yaklaşımlar gerekmektedir. Son zamanlarda bilim ve mühendislik alanında yaygın kullanılan makine öğrenme algoritmalarının GNSS teknolojisine dâhil olmasıyla, güncel sorunlara yönelik yeni yaklaşımlar, ilişkilerin açıklanıp yorumlanması ve yeni karşılaştığı olaylar için çıkarımda bulunması olanakları ortaya çıkmıştır. Bu çalışmada, zenit troposferik gecikme etkisinin, gerçek meteorolojik ve GNSS gözlem verileri ile oluşturulmuş olan makine öğrenimi modellerinden kestirilmesi amaçlanmıştır. Yöntem olarak öğrenme modellerindeki Destek Vektör Makineleri (DVM)'nin alt formu olan Destek Vektör Regresyonu (DVR) kullanılmıştır. Uygulamada, DVR modelleri oluşturulurken, gerçek ZTD değerleri ile meteorolojik parametrelerin kullanıldığı veri setine ihtiyaç vardır. Meteorolojik parametreler, IGS/EPN ağında çalışma bölgesi olarak seçilen "GOPE" istasyonundaki 2019-2020 yılları için elde edilmiştir. Gerçek ZTD verileri de aynı dönem için, gözlemlerle günde 6'şar saat aralıklı arazi koşullarında belirlenen VMF1 modelinden alınmıştır. Sonrasında toplanan tüm veriler düzenlenmiş ve DVR'in yapısındaki farklı matematiğe sahip; doğrusal (Doğrusal-DVR), polinomal (Polinomal-DVR) ve radyal-tabanlı (RTF-DVR) kernel fonksiyonlarının kullanımı ile gecikme modellerinden kestirimler gerçekleştirilmiştir. Kestirim sonuçlarına göre en yüksek başarı, R2 değeri 0,84 olan RTF-DVR modelinden hesaplanmıştır. RTF-DVR modeli, diğer DVR modellerine kıyasla daha iyi performans gösterdiği için çalışmada en uygun kestirim modeli olarak seçilmiştir. Çalışmanın son aşamasında, RTF-DVR modelinden kestirilen ZTD değerleri, IGS/EPN ağından yayınlanan troposferik ürünler ve online-PPP servisleri içerisindeki CSRS-PPP'de kestirilen ZTD değerleriyle karşılaştırılmıştır. Ayrıca tüm veriler kullanılarak oluşturulan RTF-DVR modelinin iyileştirilmesi ve örneklem grupları arasında uyuşumsuz verilerin çıkartılmasının, model sonucunu nasıl etkilediğini görmek amacıyla çok değişkenli aykırı gözlem analizlerinden LOF (Local Outlier Factor) tekniği kullanılmıştır. LOF'a göre uyuşumsuz olarak tespit edilen veriler, veri setinden çıkartılıp RTF-DVR (LOF) modeli kurulmuştur. Çalışma kapsamında oluşturulan yeni modellerin performans analizleri incelenmiştir. Analiz sonuçlarına göre, RTF-DVR (LOF) ve RTF-DVR modellerinden elde edilen ZTD değerleri ile gerçek ZTD değerleri (VMF1) arasındaki karesel ortalama hata farklarının sırasıyla ± 1,69 cm ve ± 2,08 cm olduğu saptanmıştır. Diğer değerlendirme servislerinden olan IGS/EPN ve CSRS-PPP ile karşılaştırıldığında ise, kestirim sonuçlarının birbirine oldukça yakın olduğu (~0,6 cm) belirlenmiştir. Gerçekleştirilen uygulama ile GNSS ve troposfer konularına alternatif yaklaşımların sunulabileceği görülmüştür. Makine öğreniminin, GNSS uygulamalarındaki problemlere yönelik yeni çözümler veya mevcut çözümlerin verimliliğini arttırmada önemli yer tutabileceğini göstermektedir.Article Citation - WoS: 6Citation - Scopus: 7A Deterministic Approach in Waste Management: Delineation of Potential Territories in Turkey for Industrial Symbiosis of Olive Pomace, Marble Wastes and Plastics by Integrating Fuzzy Ahp To Gis(Springer, 2022) Akar, Ali Utku; Yalpır, Şükran; Sişman, Süleyman; Göktepeli, Gamze; Yel, EsraThis study aimed to create a mapping-depended methodology in delineating the suitable territories for Industrial Symbiosis (IS) initiatives. The Geographic Information System (GIS)-integrated Fuzzy AHP (FAHP) was applied to the data of olive pomace (OP), marble processing wastes (MPW) and plastic wastes (PP/PET) generated in the whole country (Turkey). In the proposed methodology, factor identifications and their pairwise comparisons need to be made by experts. Factor weighting was implemented by FAHP method for each waste type. The highest FAHP weights were calculated as 0.57 for OP facility capacity, 0.64 for marble facility capacity and 0.41 for plastics industrial estate factors (high consistency: CR < 0.1). Potential region maps were prepared by integrating these factor weights into GIS. The second-stage FAHP was applied for determining the wastes transportation factor weights to be used in the delineation of intersecting regions. At this stage, OP and MPW have the equal and higher factor weights (0.41) than plastic wastes (0.18). The number of provinces having higher potential for establishing IS facility increased from 10 to 22 province as compared to the case with equal transportation weights. Southern Marmara, Aegean, Mediterranean and Central Anatolia were found as the regions with the higher potential for IS initiatives. The common features of the provinces in these regions are high waste generation capacities and spatially closeness to the provinces having high waste generation capacities. The proposed flexible methodology can be adapted to all types of wastes, to all number of factors (criteria) and to all countries.Article Feature Selection Applications and Model Validation for Mass Real Estate Valuation Systems(2021) Yalpır, Şükran; Şişman, Süleyman; Akar, Ali Utku; Bünyan Ünel, FatmaReal estate valuation, which has great importance in the country’s economy, is one of the issues that cannot be resolved yet. The first of the most important problems in valuation is that the feature that affects the value is not clear, and the second is that the search for methods continues because the classical valuation methods are insufficient. Therefore, mass real estate valuation systems have been revealed. In current laws, the features for valuation are not standard and many different are used in each application, even if the real estate type is the same. Also, since the market conditions in the valuation of real estate are formed according to the supply-demand relationship, the subjective approaches cause the value to affect. In the mass valuation system that needs to be established, it is essential to determine the features that affect the value according to the real estate type and the characteristic of the region to be modeled. In this study, to determine the features needed to be addressed while the mass real estate valuation, a survey application was carried out located in the Central Anatolia region of Turkey Ankara, Konya, and Kayseri province. The survey application was conducted with both experts and citizens. Using survey questions, feature selection was made with Frequency Analysis (FRA), Principal Component Analysis (PCA), Factor Analysis (FA), and Analytical Hierarchy Process (AHP) approach. A total of 21 scenarios were created with participant groups from four different methods and indexing. According to the scenarios obtained from the methods applied as a result of the survey, the Multiple Regression (linear) Analysis method (MRA) was used to examine the verification of the features under market conditions. Three study regions were determined in Konya, and 21 scenario features created were implemented in study regions by the MRA method. The results of the study were examined by applying performance analysis.Article Citation - WoS: 10Citation - Scopus: 10Performance of Spatial Interpolation Methods in Predicting Gnss Zenith Total Delay(Elsevier B.V., 2024) Akar, A.U.; Inal, C.This study proposes an alternative approach to the grid-wise VMF1 over Europe, through spatial interpolation of the site-wise VMF1 product. The performance of Ordinary Kriging and IDW was evaluated for predicting ZTD. Using 2020 data collected at 24 GNSS stations, two scenarios were constructed for February and August. Ordinary Kriging and IDW predictions were performed by making model adjustments via performance metrics and method successes were compared. The results showed that the ordinary kriging achieved a success rate of 83.2 %, while the IDW method achieved 77.1 % success. We compared the results with the grid-wise VMF1 to control the accuracy of spatial interpolations. Finally, the methods proposed as alternatives to gridded VMF1 were verified through the CSRS-PPP(ZTD). Ordinary-Kriging (RMSEFeb: [0.92–2.95 cm]; RMSEAug: [1.76–3.85 cm]) was in better compliance with the CSRS-PPP(ZTD), compared to the grid-wise VMF1 (RMSEFeb: [1.59–3.01 cm]; RMSEAug: [2.32–3.93 cm]). These results support that the quality of gridded products can be improved by spatial interpolations. © 2024 Elsevier LtdArticle Citation - WoS: 13Citation - Scopus: 14The Novelty Hybrid Model Development Proposal for Mass Appraisal of Real Estates in Sustainable Land Management(TAYLOR & FRANCIS LTD, 2023) Şişman, Süleyman; Akar, Ali Utku; Yalpır, ŞükranIn this study, a new methodology has been developed for a sustainable mass appraisal system. A mathematical model was created with the combination of the Cobb-Douglas and the linear regression model. With the Analytic Hierarchy Process (AHP) method, real estate value criteria were grouped and weighted in a hierarchical structure. The weights obtained with AHP were integrated into the coefficients regarding the criteria weights and densities in the Cobb-Douglas hybrid model. The new hybrid model was confirmed with the features and price equivalents of 435 parcels for sale from the market. Besides, the model analysis results were compared with the Multiple Regression Analysis (MRA) modelling using market prices. While creating the methodology, Geographic Information Systems (GIS) was used to organize the geographic and regional data of the region. After developing the new hybrid model, criteria groups that developed the model and relevant sub-criteria were evaluated using Pearson's correlation analysis.Article Evaluation of Carbon Footprint Effect in Land Consolidation for Sustainable Agricultural Planning: A Research on Transportation and Intra-Parcel Tractor Maneuvers(Elsevier Sci Ltd, 2025) Uyan, Mevlut; Akar, Ali Utku; Yalpir, SukranCarbon emission is a major driver to climate change, posing significant challenges to global sustainability efforts. Agriculture, as a key sector, plays a substantial role in greenhouse gas (GHG) emissions, with land fragmentation further exacerbating this issue by increasing fuel consumption and CO2 emissions during farming activities. Land consolidation (LC) has emerged as an effective strategy to mitigate these impacts by reducing the spatial dispersion of parcels and improving mechanization efficiency. This study focuses on a consolidation area in Konya, a vital agricultural region in T & uuml;rkiye. It examines the effects of changes in routes and tractor maneuvers before and after LC on the fuel consumption of tractors and, accordingly, the carbon footprint (CF). Using network analysis and the Tier-1 method for CF calculations, the study found a 40% reduction in total CO2 emissions due to decreased transportation distances. The average CF per enterprise decreased by 38.26%, demonstrating the potential of LC to make a substantial contribution to GHG reduction. These findings highlight LC's role in fostering sustainable agricultural planning and environmental conservation, providing a pathway for long-term carbon reduction and climate resilience. Further research is recommended to develop region-specific LC models integrating socio-economic and climatic variables for enhanced sustainability outcomes.Article Citation - WoS: 7Citation - Scopus: 10Evaluating Lake Water Quality With a Gis-Based Mcda Integrated Approach: a Case in Konya/Karapınar(Springer Heidelberg, 2024) Akar, Ali Utku; Şişman, Süleyman; Ülkü, Harika; Yel, Esra; Yalpır, ŞükranConsidering water quality is an essential requirement in terms of environmental planning and management. To protect and manage water resources effectively, it is necessary to develop an analytical decision-support system. In this study, a systematic approach was suggested to evaluate the lake water quality. The methodology includes the prediction of the values in different locations of the lakes from experimental data through inverse distance weighting (IDW) method, creation of maps by using Geographic Information System (GIS) integrated with analytic hierarchy process (AHP) from multi-criteria decision analysis (MCDA), reclassification into five class, combining the time-related spatial data into a single map to predict the whole lake water quality from the data of sampling points, and finally overlapping the final maps with topography/geology and land use. The proposed approach was verified and presented as case study for Meke and Acigol Lakes in Konya/Turkey which were affected by human and natural factors although they have ecological, hydromorphological, and socio-economic importance. In the proposed approach, categorizing water quality parameters as hardness and minerals, substrates and nutrients, solids content, metals, and oil-grease groups was helpful for AHP with the determined group weights of 0.484, 0.310, 0.029, and 0.046, respectively. Assigning weights within each group and then assigning weights between groups resulted in creating accurate final map. The proposed approach is flexible and applicable to any lake water quality data; even with a limited number of data, the whole lake water quality maps could be created for assessment.
