Fatma Bünyan Ünel Fatma Bünyan Ünel MÜHENDİSLİK FAKÜLTESİ HARİTA MÜHENDİSLİĞİ BÖLÜMÜ HARİTA MÜHENDİSLİĞİ ANABİLİM DALI
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Statistical and Visual Evaluation of Artificial Neural Networks and Multiple Linear Regression Performances in Estimating Reference Crop Evapotranspiration for Mersin

Unel, Fatma Bunyan | Kusak, Lutfiye | Yakar, Murat

This is study aimed to create a model for calculating the total reference crop evapotranspiration (ETo) in Mersin Province from May 2015 to 2020 and to generate maps using spatial analysis. Lemons from citrus play a significant role inMersin’s agriculture, and because of lemons’ sensitivity to temperature, ETo is essential for them. Itwas observed that the ETo value (EToPM) calculated using the Penman-Monteith (PM)method increased over the years. A model was developed using data from çâ Automated Weather Observing Systems (AWOS) in Mersin, Türkiye, which is located in a semi-arid climate zone. the model was created using Multiple Linear Regression (MLR) and artificial neural network (ANN) methods. the station climate data were divided into training and test datasets separately and collectively, and ETo values were estimated with different combinations using three scenarios and sixmodel constructs. the dataset was divided into training (2015 –2018) and testing (2019–2020). ANN1 and MLR1 are analyses of individual AWOS, while the other models are analyses of all AWOS together. The statistical performance analysis involved a comparison of the Rò,Mean Absolute Error (MAE),Mean Absolute Percentage Error (MAPE), and RootMean Square Error (RMSE) values.The analysis results in...

SWOT analysis of green building systems in real estate development

Ünel, Fatma Bünyan

Sustainable and green facts are important for leaving a healthy environment for future generations. These facts are like sustainable development, sustainable city, green building, green economy, and so on. Green building is an innovative solution to sustainable urban planning problems. In this study, green building, sustainability, development processes are examined, and the opportunities and threats of green buildings with their strengths and weaknesses are determined by SWOT analysis. Besides, the construction cost and energy savings of a green building are compared to the traditional building. As a result of SWOT analysis, besides its significant strengths such as sustainability of green buildings, low energy consumption, and independence of energy consumption, high initial investment cost was determined as the most critical weakness. The increase in building values and the emergence of new business ventures are considered opportunities for green buildings, and the perception of green buildings as luxury is considered a threat. Ultimately, this study reveals the critical significance of transitioning towards sustainable construction practices, supported by compelling numerical evidence. Despite the initial higher investment costs, green buildings exhibit substantial long-term ...

Apriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping

Çelik, Mehmet Özgür | Kuşak, Lütfiye | Ünel, Fatma Bünyan | Alptekin, Aydın | Yakar, Murat

In this paper, an inventory of the landslide that occurred in Karahacılı at the end of 2019 was created and the pre-landslide conditions of the region were evaluated with traditional statistical and spatial data mining methods. The current orthophoto of the region was created by unmanned aerial vehicle (UAV). In this way, the landslide areas in the region were easily determined. According to this, it was determined that the areas affected by the landslides had an average slide of 26.56 m horizontally. The relationships among the topographic, hydrographic, and vegetative factors of the region were revealed using the Apriori algorithm. It was determined that the areas with low vegetation in the study area with 55% confidence were of a Strong Slope feature from the Apriori algorithm. In addition, the cluster distributions formed by these factors were determined by K-means. Among the five clusters created with K-means, it was determined that the study area was 38% in the southeast, had a Strong Slope, Low Vegetation, Non-Stream Line, and a slope less than 140 m. K-means results of the study were made with performance metrics. Average accuracy, recall, specificity, precision, and F-1 score were found as 0.77, 0.69, 0.84, and 0.73 respectively.