Yazar "Genç, Levent Genc" seçeneğine göre listele

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    Analyzing Agricultural Land Price Prediction Using Linear Regression and XGBoost Machine Learning Algorithms: A Case Study of Çanakkale
    (2025) Doğan, Simge; Genç, Levent Genc; Yucebas, Sait Can; Uşaklı, Metin
    Agricultural lands are known not only as agricultural production areas but also as areas with high income expectations as an investment tool. In Turkey, recent fluctuations in economic indicators such as the euro, dollar, and gold, along with increasing investment demand, have caused agricultural land prices to rise uncontrollably. Controlling land price increases is important for preventing the misuse of agricultural lands. The sustainable management of agricultural lands and price stability are closely related to the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 and 15, “Sustainable Cities and Communities” and “Life on Land.” In this context, accurately predicting prices is important for minimizing price fluctuations in agricultural lands for investors and landowners and supporting sustainable development. In general, the Multiple Linear Regression (MLR) model is considered one of the effective traditional methods for predicting real estate prices. However, depending on the data, more reliable results can be obtained than with powerful deep learning models such as the Extreme Gradient Boosting (XGBoost) algorithm, which exhibits superior prediction performance. This study aims to compare the MLR and XGBoost algorithms to predict agricultural land prices in villages located in the central district of Çanakkale and to examine daily fluctuations in economic indicators such as the dollar, gold, and euro. The results showed that XGBoost (R2 = 0.66) has an advantage in terms of coefficient of determination values compared to MLR (R2 = 0.01). Accurate price prediction for agricultural lands will help control fluctuations in land prices. Additionally, it will support farmers and investors in making informed decisions for a sustainable agricultural economy.
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    Determination of Potential Solid and Construction &Demolition Waste Landfill Areas Using GIS-Based AHP in Gökçeada
    (2025) Sedefoğlu, Bilge; Oz, Nilgun Ayman; Genç, Levent Genc
    The identification of locations for both municipal solid waste (MSW) landfill and construction and demolition waste (CDW) disposal necessitates a comprehensive spatial planning strategy, particularly in environmentally sensitive and physically constrained areas. Gökçeada, an island characterized by limited developable land and seasonal population variations due to tourism, requires integrated planning for MSW and CDW. This research utilized a multi-criteria decision-making model (MCDM) that integrates the Analytic Hierarchy Process (AHP) with Geographic Information Systems (GIS) to determine appropriate sites for both forms of waste. The findings indicate that merely 3.61% of the island is appropriate for municipal solid waste landfill and 3.67% for CDW disposal site, with extremely favorable regions accounting for 1.04% and 0.61%, respectively. 3 locations; Area 1, Area 2, and Area 3 were recognized as feasible choices, each presenting unique spatial and logistical benefits. Moreover, the designated locations align with the anticipated MSW accumulation volume by 2058. This dual evaluation facilitates optimal land utilization, mitigates environmental repercussions, and offers a reproducible framework for sustainable waste management in island regions.
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    Predicting Winter Wheat Yield using Landsat 8-9 Based Vegetation Indices in Semi-Arid Regions
    (2025) Civelek, Neslişah; Genç, Levent Genc; Akcay, Ozgun
    This study investigated the prediction of winter wheat yield in cultivation regions of Kumkale (Batakovası) Plain in Çanakkale Province, Türkiye, utilizing Landsat 8-9 imagery-based Vegetation Indices (VIs) along- side Machine Learning (ML) methodologies. The VIs dataset was created by calculating images collected during the 2022 and 2023 growth seasons. The resulting dataset was employed in a C4.5 Decision Tree (DT) algorithm to predict winter wheat yield. The findings indicated that winter wheat yield could be predicted in April for fields classified as ‘Low Yield, ’Medium Yield,’ and ‘High Yield’ utilizing all indices except for Enhanced Vegetation Index (EVI) and Soil Adjusted Vegetation Index (SAVI). Interestingly, High Yield’ fields could also be predicted in March using the EVI index and in February using the SAVI index. In the winter wheat yield estimation, NDVI with a performance rate of 97.5% was able to determine \"High Yield,\" \"Medium Yield,\" and \"Low Yield\" in April (heading-blooming), while the lowest performance was with EVI at 77.50%, determining \"High Yield” in April (heading-blooming), \"Medium Yield\" (tilling-jointing) in February, and \"Low Yield”. (tilling-jointing) in March. The study concluded that winter wheat yields can be predicted using VIs independently of climate data. Future research will concentrate on assessing yield predictions for additional crops by employing various ML algorithms alongside climate data and VIs derived from higher-resolution satellite imagery.