Doğal Gaz Boru Hattı İnşaatı Maliyetlerinin Çoklu Doğrusal Regresyon ve K-En Yakın Komşuluk Yöntemleri İle Tahmini

Abstract

Bu çalışmada, Türkiye sınırları içerisinde yapılacak olan doğal gaz boru hattı (DGBH) maliyetlerinin ön tahmini için makine öğrenmesi algoritmaları kullanılarak modeller geliştirilmiştir. Bunun için, 1997-2022 yılları arasında Türkiye'de tamamlanmış DGBH projelerinden elde edilen veriler kullanılmıştır. Projelerin boru çapı, hat uzunluğu, hat vanası sayısı, take-off vana sayısı ve pig istasyonu sayısı gibi değişkenleri, maliyet tahmininde bağımsız değişkenler olarak belirlenmiştir. Veri setinin nicel anlamda yetersiz ve veri kalitesinin ortalama bir seviyede olmasından dolayı, klasik makine öğrenmesi tahmin süreçleri yürütülememiştir. Bu nedenle, mevcut veri seti eğitim ve test bölümlerine ayrılmadan, bütün veri kullanılarak çalışılmış ve Çoklu Doğrusal Regresyon (ÇDR) ile K-En Yakın Komşu (KNN) algoritmalarına konumlandırıldığında modelin uygun bir şekilde performans gösterip göstermediği incelenmiştir. Bu çalışma, ileride veri kalitesinin ve sayısının artması durumunda, klasik makine öğrenmesi tahmin süreçlerinin yürütülüp yürütülemeyeceği konusunda ön fikir vermesi amacıyla gerçekleştirilmiştir. Her iki farklı yöntem denemesinde de benzer ve ortalama düzeyde belirleme katsayıları (R²) elde edilmiştir. Sonuç olarak, bu çalışmada, DGBH projelerinde ön maliyet tahminlerinin hassasiyetini iyileştirmek için ÇDR ve KNN yöntemlerinin etkinliği karşılaştırılmış ve sektöre önemli bir katkı sağlayacağı değerlendirilmiştir. Gelecekte yapılacak çalışmaların daha geniş veri setleri ve farklı model teknikleri kullanarak maliyet tahminlerinin doğruluğunu artırabileceği ve sektör paydaşlarına yol gösterici olabileceği öngörülmektedir.

Keywords

• Doğal gaz boru hatları
• Makine öğrenmesi
• Maliyet tahmini

Estimation of Natural Gas Pipeline Construction Costs Using Multiple Linear Regression and K-Nearest Neighbor Methods

Abstract

In this study, models were developed using machine learning algorithms for the preliminary estimation of natural gas pipeline (NGP) costs within the borders of Türkiye. For this purpose, data obtained from NGP projects completed in Türkiye between 1997 and 2022 were used. Variables such as pipe diameter, line length, number of line valves, number of take-off valves and number of pigging stations of the projects were determined as independent variables in the cost estimation. Since the data set was quantitatively insufficient and the data quality was at an average level, classical machine learning estimation processes could not be carried out. For this reason, the existing data set was studied using the entire data without dividing it into training and test sections, and it was examined whether the model performed appropriately when positioned in Multiple Linear Regression (MLR) and K-Nearest Neighbor (KNN) algorithms. This study was carried out to provide a preliminary idea about whether classical machine learning estimation processes can be carried out in the future if the data quality and number increase. Similar and average coefficients of determination (R²) were obtained in both different method trials. As a result, in this study, the effectiveness of the MLR and KNN methods was compared to improve the accuracy of preliminary cost estimates in NGP projects and it was evaluated that it will make a significant contribution to the sector. It is anticipated that future studies can increase the accuracy of cost estimates by using larger data sets and different model techniques and can guide the sector stakeholders.

Keywords

• Natural gas pipelines
• Machine-learning
• Cost estimate

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