Mihra GülerOnur AdakMehmet Serdar ErdoğanOzgur KabadurmusGüler, MihraAdak, OnurErdogan, Mehmet SerdarKabadurmus, OzgurN.M. Durakbasa , M.G. Gençyılmaz2025-10-0620229789819650583, 9783031991585, 9783031948886, 9789819667314, 9789811937156, 9783030703318, 9789811622779, 9789811969447, 9789819701056, 97898197480519783030904203978303090421021954364, 219543562195-43642195-435610.1007/978-3-030-90421-0_612-s2.0-85119892546https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119892546&doi=10.1007%2F978-3-030-90421-0_61&partnerID=40&md5=578c0799786626c2029e070edd91be65https://gcris.yasar.edu.tr/handle/123456789/8845https://doi.org/10.1007/978-3-030-90421-0_61Forecasting the number of damaged containers is crucial for a maritime company to effectively plan future port operations. The purpose of this study is to forecast damaged container entries and exits. In this paper we worked with a global logistics company in Turkey. Comparisons between ports were made using the company’s internal port operations data and externally available data. The external data that we used are Turkey’s GDP exchange rates (USD/EUR) import and export data TEU of Mersin port and the total TEU of Turkey’s ports (2015–2020). Our aim is to forecast the number of damaged containers at a specific port (Mersin Turkey) using different machine learning methods and find the best method. We used Linear Regression Boosted Decision Tree Regression Decision Forest Regression and Artificial Neural Network Regression algorithms. The performances of these methods were evaluated according to various metrics such as R2 MAE RMSE RAE and RSE. According to our results machine learning methods can forecast container demand effectively and the best performing method is Boosted Decision Tree regression. © 2022 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessContainer Demand, Forecasting, Logistics, Machine Learning, Regression, Containers, Decision Trees, Machine Learning, Neural Networks, Regression Analysis, Boosted Decision Trees, Container Demand, Decision Tree Regression, Exchange Rates, Global Logistics, Logistics Company, Machine Learning Methods, Machine-learning, Port Operations, Regression, ForecastingContainers, Decision trees, Machine learning, Neural networks, Regression analysis, Boosted decision trees, Container demand, Decision tree regression, Exchange rates, Global logistics, Logistics company, Machine learning methods, Machine-learning, Port operations, Regression, ForecastingLogisticsMachine LearningContainer DemandForecastingRegressionConference Object