title: ÐÑÐµÐ½ÐºÐ° ÑÐºÐ¾ÑÐ¾ÑÑÐ¸ Ð´Ð²Ð¸Ð¶ÐµÐ½Ð¸Ñ ÑÑÐ´Ð¾Ð² Ð½Ð° Ð¡ÐµÐ²ÐµÑÐ½Ð¾Ð¼ Ð¼Ð¾ÑÑÐºÐ¾Ð¼ Ð¿ÑÑÐ¸ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, Ð¿ÑÐ¾ÑÐµÑÑ-Ð¾ÑÐ¸ÐµÐ½ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¸ Ð³Ð¸Ð±ÑÐ¸Ð´Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð²
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Estimation of the speed of ships on the Northern Sea Route based on machine learning methods, process-oriented models and hybrid methods
Creators: name: ÐÑÐµÐ¼ÐµÐ½ÐºÐ¾, ÐÐ°Ð½Ð¸Ð»Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¡ÑÐ´Ð°
ÐÐ²Ð¸Ð¶ÐµÐ½Ð¸Ðµ
Ð¡ÐµÐ²ÐµÑÐ½ÑÐ¹ Ð¼Ð¾ÑÑÐºÐ¾Ð¹ Ð¿ÑÑÑ
Ð¾ÑÐµÐ½ÐºÐ° Ð²ÑÐµÐ¼ÐµÐ½Ð¸ Ð¿ÑÐ¾Ñ Ð¾Ð¶Ð´ÐµÐ½Ð¸Ñ Ð¼Ð°ÑÑÑÑÑÐ°
Ð´ÐµÑÐµÐ²ÑÑ ÑÐµÑÐµÐ½Ð¸Ð¹
ÑÐ»ÑÑÐ°Ð¹Ð½ÑÐ¹ Ð»ÐµÑ
Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½ÑÐ¹ Ð±ÑÑÑÐ¸Ð½Ð³
Ð¿ÑÐ¾ÑÐµÑÑ-Ð¾ÑÐ¸ÐµÐ½ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÐµ Ð¼Ð¾Ð´ÐµÐ»Ð¸
Ð³Ð¸Ð±ÑÐ¸Ð´Ð½ÑÐµ Ð¼ÐµÑÐ¾Ð´Ñ
Northern sea route
estimation of the time of arrival
decision trees
random forest
gradient boosting
process-oriented models
hybrid methods

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description: The subject of the master thesis is: "Vessels speed estimation on the Northern Sea route based on machine learning methods, process-based models and hybrid methods". This is a first benchmark study on estimating ship speeds and voyage time utilizing AIS data and ice conditions data from the Northern Sea Route. Specifically, the benchmark examines five methods representing three categories of predictive modeling approaches (data-driven, physics-driven, and hybrid) with special attention to making the benchmark as unbiased as possible. The paper presents a comparison between predictions and actual speed records in terms of RMSE and Pearson correlation coefficient in different geographical areas on the Northern Sea Route during different times of the year as well as a difference between actual and predicted voyage time. The goals of this work are implementation of the new methods of speed of vessels estimation based on different available datasets and qualitative comparison of their applicability with existing methods. As a result of the comparison hybrid model showed the best results on the most interesting and most difficult voyage in terms of machine learning methods validation.