@article{Yamane_Kano_2023,
	author = {Yamane, Daiki and Kano, Toshiyuki},
	title = {Prediction of Ship's Speed Through Ground Using the Previous Voyage's Drift Speed},
	journal = {TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation},
	volume = {17},
	number = {1},
	pages = {129-137},
	year = {2023},
	url = {./Article_Prediction_of_Ships_Speed_Through_Yamane,65,1283.html},
	abstract = {In recent years, 'weather routing' has been attracting increasing attention as a means of reducing costs and environmental impact. In order to achieve high-quality weather routing, it is important to accurately predict the ship's speed through ground during a voyage from ship control variables and predicted data on weather and sea conditions. Because sea condition forecasts are difficult to produce in-house, external data is often used, but there is a problem that the accuracy of sea condition forecasts is not sufficient and it is impossible to improve the accuracy of the forecasts because the data is external. In this study, we propose a machine learning method for predicting speed through ground by considering the actual values of the previous voyage’s drift speed for ships that regularly operate on the same route, such as ferries. Experimental results showed that this method improves the prediction performance of ship’s speed through ground.},
	doi = {10.12716/1001.17.01.13},
	issn = {2083-6473},
	publisher = {Gdynia Maritime University, Faculty of Navigation},
	keywords = {Weather Routing, Route Planning, Tidal Current, Machine Learning, Ship's Speed Estimation, LightGBM, Drift Speed, Algorithm}
}