Prediction of main particulars of container ships using artificial intelligence algorithms

Majnarić, Darin; Šegota, Sandi Baressi; Lorencin, Ivan; Car, Zlatan

doi:10.1016/j.oceaneng.2022.112571

Cited by 12 publications

(14 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ekinci (6) was predicting the main parameters of oil tankers as well by using ANN and the method that has shown as most successful was the Model Trees (M5P) method. When considering ship types most of the research was been conducted on container ships, Majnaric et al (10) developed a database of 250 container ships which was analyzed with ANN to get high accurate estimation of initial ship values. Additionally, research was also conducted on the utilization of synthetic data to get to the amount of data that can be usable for ANN (9).…”

Section: Introductionmentioning

confidence: 99%

“…When considering ship types most of the research was been conducted on container ships, Majnaric et al (10) developed a database of 250 container ships which was analyzed with ANN to get high accurate estimation of initial ship values. Additionally, research was also conducted on the utilization of synthetic data to get to the amount of data that can be usable for ANN (9). The research focused on the estimation of ship length between perpendiculars was conducted by Cepowski et al (2) and by using ANN they have produced two equations for preliminary design purposes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of main engine power of oil tankers using artificial intelligence algorithms

Majnarić,

Anđelić,

Šegota

et al. 2024

IMDC

View full text Add to dashboard Cite

In the preliminary ship design, the accurate determination of a vessel’s main engine power is one of the most critical aspects next to service speed, main particulars, and cargo capacity. However, this task can be quite intricate due to its reliance on an extremely great number of influencing factors. In the research that is presented in this paper dataset of 357 oil tankers was gathered and developed to research the idea in which genetic programming is applied to the mentioned dataset to obtain mathematical equations (MEs) that can estimate the ship’s main engine power with high accuracy. The highest estimation accuracy of MEs is achieved by tuning the GP hyperparameter values through the random hyperparameter search (RHS) method. The initial dataset was divided into train and test datasets in a 70:30 ratio. The train dataset was used to train GP in a 5-fold cross-validation process and after the process was done the obtained MEs were evaluated on the test dataset. To evaluate the GP training testing process several evaluation metrics were used i.e., coefficients of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and length of obtained MEs. The conducted investigation showed that GP generated MEs that can estimate ship main engine power with high accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of main engine power of oil tankers using artificial intelligence algorithms

Majnarić,

Anđelić,

Šegota

et al. 2024

IMDC

View full text Add to dashboard Cite

show abstract

“…The traditional approach involves using statistical regression equations based on data from a comparable ship with known features [3]. In the preliminary design phase, designers play a crucial role in defining a ship's major features based on implicit client requirements, including draft length, service speed range, and bollard pull capacity.…”

Section: Introductionmentioning

confidence: 99%

“…Neural networks are poised to emerge as a pivotal tool in the initial phase of ship design, offering, among other capabilities, the estimation of crucial ship particulars [3]. A comprehensive examination of machine learning applications in sustainable ship design and operation is outlined in a study by [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Ship Main Particulars for Harbor Tugboats Using a Bayesian Network Model and Non-Linear Regression

Karaçay,

Karatuğ,

Uyanık

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Determining the key characteristics of a ship during the concept and preliminary design phases is a critical and intricate process. In this study, we propose an alternative to traditional empirical methods by introducing a model to estimate the main particulars of diesel-powered Z-Drive harbor tugboats. This prediction is performed to determine the main particulars of tugboats: length, beam, draft, and power concerning the required service speed and bollard pull values, employing Bayesian network and non-linear regression methods. We utilized a dataset comprising 476 samples from 68 distinct diesel-powered Z-Drive harbor tugboat series to construct this model. The case study results demonstrate that the established model accurately predicts the main parameters of a tugboat with the obtained average of mean absolute percentage error values; 6.574% for the Bayesian network and 5.795%, 9.955% for non-linear regression methods. This model, therefore, proves to be a practical and valuable tool for ship designers in determining the main particulars of ships during the concept design stage by reducing revision return possibilities in further stages of ship design.

show abstract

Comparison of model selection and data bias on the prediction performance of purpleback flying squid (Sthenoteuthis oualaniensis) fishing ground in the Northwest Indian Ocean

Han,

Jiang,

Xiang

et al. 2024

Ecological Indicators

View full text Add to dashboard Cite

Prediction of main particulars of container ships using artificial intelligence algorithms

Cited by 12 publications

References 42 publications

Prediction of main engine power of oil tankers using artificial intelligence algorithms

Prediction of main engine power of oil tankers using artificial intelligence algorithms

Prediction of Ship Main Particulars for Harbor Tugboats Using a Bayesian Network Model and Non-Linear Regression

Comparison of model selection and data bias on the prediction performance of purpleback flying squid (Sthenoteuthis oualaniensis) fishing ground in the Northwest Indian Ocean

Contact Info

Product

Resources

About