Container traffic forecasting is important for the operations and the design steps of a seaport facility. In this study, performances\nof the novel soft computing models were compared for the container traffic forecasting of principal Turkish seaports (Istanbul,\nIzmir, and Mersin seaports) with excessive container traffic. Four forecasting models were implemented based on Artificial\nNeuralNetwork with Artificial Bee Colony and Levenberg-Marquardt Algorithms (ANN-ABC and ANN-LM),MultipleNonlinear\nRegression with Genetic Algorithm (MNR-GA), and Least Square Support Vector Machine (LSSVM). Forecasts were carried out\nby using the past records of the gross domestic product, exports, and population of the Turkey as indicators of socioeconomic\nand demographic status. Performances of the forecasting models were evaluated with several performance metrics. Considering\nthe testing period, the LSSVM, ANN-ABC, and ANN-LM models performed better than theMNR-GA model considering overall\nfitting and prediction performances of the extreme values in the testing data. The LSSVM model was found to be more reliable\ncompared to the ANN models. Forecasting part of the study suggested that container traffic of the seaports will be increased up to\n60%, 67%, and 95% at the 2023 for the Izmir, Mersin, and Istanbul seaports considering official growth scenarios of Turkey.
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