Artificial Neural Network Approach for Main Engine Power Prediction of General Cargo Vessels

Yıl 2024, , 113 - 129, 30.12.2024

Emrullah Çirçir , Samet Gürgen

https://doi.org/10.54410/denlojad.1542984

Öz

Before the physical constructing of a ship will start, it must first go through a multistage design process. Determining the ship's main engine power is a critical stage in the concept design process. This work established a model to predict for the main engine power of general cargo ships. The model input parameters included ship length overall, breadth, gross tonnage, DWT and ship service speed. In the training stage of the model, Levenberg-Marquardt optimization algorithm was used. After many training attempts with various numbers of hidden neurons, the structure with 22 hidden neurons showed the best performance. R values for the test set were 0.986, 0.988 for validation, and 0.992 for training, according to regression analysis. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values remained consistently low across all normalized datasets, ranging from 0.0128 to 0.0148 for MAE and 0.0178 to 0.0238 for RMSE. These results underscore the model's robust predictive capabilities.

Anahtar Kelimeler

General cargo vessel, Main engine power, Prediction model, Artificial neural network

Genel Kargo Gemilerinin Ana Makine Gücü Tahmini İçin Yapay Sinir Ağı Yaklaşımı

Öz

Bir geminin fiziki inşasına başlanmadan önce çok aşamalı bir tasarım sürecinden geçmesi gerekmektedir. Geminin ana makine gücünün belirlenmesi kavram dizaynı sürecinde kritik bir aşamadır. Bu çalışma, genel kargo gemilerinin ana makine gücünü tahmin etmek için bir model sunmaktadır. Modelin girdi parametreleri gemi tam boyu, genişlik, gros tonaj, dedveyt tonaj ve gemi hızını içermektedir. Modelin eğitim aşamasında Levenberg-Marquardt optimizasyon algoritması kullanılmıştır. Çeşitli sayıda gizli nöronla yapılan birçok eğitim denemesinden sonra 22 gizli nöronlu yapı en iyi performansı göstermiştir. Regresyon analizine göre test seti için R değerleri 0.986, doğrulama için 0.988 ve eğitim için 0.992 olarak hesaplanmıştır. Ortalama Mutlak Hata (MAE) ve Ortalama Karekök Hata (RMSE) değerleri, MAE için 0.0128 ile 0.0148 ve RMSE için 0.0178 ile 0.0238 arasında değişerek tüm normalize edilmiş veri kümelerinde tutarlı bir şekilde düşük kalmıştır. Bu sonuçlar, modelin sağlam tahmin yeteneklerini vurgulamaktadır.

Anahtar Kelimeler

Genel kargo gemisi, Ana makine gücü, Tahmin modeli, Yapay sinir ağı

Kaynakça

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