Calculating the main engine power of fishing vessels with artificial neural networks analysis

Abstract

Fishing vessels carry out the majority of the world's fishing. All other vessel types will be inspired by advancements in fishing vessels. Determining the ship's main engine power is crucial for both energy efficiency and environmental considerations because of this. The fishing and shipping industries are just now starting to realize how important artificial intelligence technology is. In this investigation, a model of an artificial neural network (ANN) was used to forecast the power of the main engine and the emissions of pollutants from fishing vessels. The model takes into account 12 characteristics, including the maximum speed, width, year of construction, kind of ship, overall length, displacement-light ship, DWT, gross tonnage, engine cylinder, and engine stroke. The ANN analysis has been trained to produce reliable results using the data of 800 fishing vessels, which is quite a lot in comparison to the research in the literature. In order to produce the fewest errors and most accurate results, numerous artificial neural network models have been designed. When the findings of the artificial neural network study were compared to actual values, it became clear that the MSE and regression results had produced very accurate results. Various hidden neuron numbers have been tried, along with performance outcomes, in order to make accurate predictions with the greatest degree of precision in ANN analysis. The created model can be applied to research on fishing vessel energy utilization and fuel consumption.