Harnessing Publicly Available Information with Data Science to Extract the Operational Profile of a Vessel

2019 ◽  
Author(s):  
R Bakker ◽  
B T W Mestemaker ◽  
J T M Wijnands
Keyword(s):  
Fuel Consumption ◽  
Data Science ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Automatic Identification ◽  
Identification System ◽  
Weather Information ◽  
Operational Profile ◽  
Drive Systems ◽  
Available Information

The optimal design of an efficient and cost-effective vessel requires extensive knowledge about its intended operation. However, this information is not always available or accessible for a ship designer/yard. This often results in a vessel which is less well suited for the job than it should be. The vessel is over specified for the required task and as a result more expensive than it could be for the client to buy and operate. A platform was developed which can extract the entire operational profile of a trailing suction hopper dredger with as little information as possible. The information used consists of publicly available data, such as that of the automatic identification system, weather information and sea charts. The platform uses machine learning algorithms to determine the vessel task, time spent in the task and the vessel uptime. Combining these results with additional knowledge of dredgers and their drive systems allows for an estimation of both the dredger production and the power and fuel consumption. The paper discusses the methods used in the platform to extract the operational profile from the publicly available data and how this results in a power and fuel consumption estimation. The results of the platform will be validated with information available from two trailing suction hopper dredgers.

scholarly journals A modelling system for the exhaust emissions of marine traffic and its application in the Baltic Sea area

2009 ◽  
Vol 9 (4) ◽  
pp. 15339-15373 ◽  
Author(s):  
J.-P. Jalkanen ◽  
A. Brink ◽  
J. Kalli ◽  
H. Pettersson ◽  
J. Kukkonen ◽  
...  
Keyword(s):  
Baltic Sea ◽  
Fuel Consumption ◽  
Exhaust Emissions ◽  
Automatic Identification ◽  
Identification System ◽  
The Baltic Sea ◽  
Support Tool ◽  
Marine Traffic ◽  
The Baltic ◽  
Modelling System

Abstract. A method is presented for the evaluation of the exhaust emissions of marine traffic, based on the messages provided by the Automatic Identification System (AIS), which enable the identification and location determination of ships. The use of the AIS data enables the positioning of ship emissions with a high spatial resolution, which is limited only by the inaccuracies of the Global Positioning System (typically a few metres) that is used in vessel navigation. The emissions are computed based on the relationship of the instantaneous speed to the design speed, and these computations also take into account the detailed technical information of the ships' engines. The modelling of emissions is also based on a few basic equations of ship design, including the modelling of the propelling power of each vessel in terms of its speed. We have also investigated the effect of waves on the consumption of fuel, and on the emissions to the atmosphere. The predictions of fuel consumption were compared with the actual values obtained from the shipowners. For a RoPax vessel, the predicted and reported values of fuel consumption agreed within an accuracy of 6%. According to the data analysis and model computations, the emissions of NOx, SOx and CO2 originating from ships in the Baltic Sea in 2007 were in total 400 kt, 138 kt and 19 Mt, respectively. A breakdown of emissions by flag state, ship's type and year of construction is also presented. The modelling system can be used as a decision support tool in the case of issues concerning, e.g., health effects caused by shipping emissions, the construction of emission-based fairway dues systems or emissions trading. The computation of emissions can also be automated, which will save resources in constructing emission inventories. Both the methodologies and the emission computation program can be applied in any sea region in the world, provided that the AIS data from that specific region are available.

scholarly journals Introduction to Coastal HF Maritime Surveillance Radars

2019 ◽  
Vol 26 (3) ◽  
pp. 153-162 ◽  
Author(s):  
Dimov Stojce Ilcev
Keyword(s):  
Ground Truth ◽  
Cost Effective ◽  
Hf Radar ◽  
Automatic Identification ◽  
Identification System ◽  
Ligurian Sea ◽  
Maritime Surveillance ◽  
Detection And Tracking ◽  
Multi Target Tracking ◽  
Ship Classification

Abstract This paper presents the main technical characteristics and working performances of coastal maritime surveillance radars, such as low-power High-Frequency Surface Wave Radars (HFSWR) and Over the Horizon Radars (OTHR). These radars have demonstrated to be a cost-effective long-range early-warning sensor for ship detection and tracking in coastal waters, sea channels and passages. In this work, multi-target tracking and data fusion techniques are applied to live-recorded data from a network of oceanographic HFSWR stations installed in Jindalee Operational Radar Network (JORN), Wellen Radar (WERA) in Ligurian Sea (Mediterranean Sea), CODAR Ocean Sebsorsin and in the German Bight (North Sea). The coastal Imaging Sciences Research (ISR) HFSWR system, Multi-static ISR HF Radar, Ship Classification using Multi-Frequency HF Radar, Coastal HF radar surveillance of pirate boats and Different projects of coastal HF radars for vessels detecting are described. Ship reports from the Automatic Identification System (AIS), recorded from both coastal and satellite Land Earth Stations (LES) are exploited as ground truth information and a methodology is applied to classify the fused tracks and to estimate system performances. Experimental results for all above solutions are presented and discussed, together with an outline for future integration and infrastructures.

scholarly journals MARITIME BIG DATA ANALYSIS WITH ARLAS

2021 ◽  
Vol XLVI-4/W2-2021 ◽  
pp. 71-76
Author(s):  
W. Gautier ◽  
S. Falquier ◽  
S. Gaudan
Keyword(s):  
Big Data ◽  
Open Source ◽  
Big Data Analysis ◽  
Machine Learning Algorithms ◽  
Maritime Transportation ◽  
Automatic Identification ◽  
Identification System ◽  
Maritime Industry ◽  
Huge Amount ◽  
Processing Platform

Abstract. The maritime industry has become a major part of globalization. Political and economic actors are meeting challenges regarding shipping and people transport. The Automatic Identification System (AIS) records and broadcasts the location of numerous vessels and delivers a huge amount of information that can be used to analyze fluxes and behaviors. However, the exploitation of these numerous messages requires tools based on Big Data principles.Acknowledgement of origin, destination, travel duration and distance of each vessel can help transporters to manage their fleet and ports to analyze fluxes and focus their investigations on some containers based on their previous locations. Thanks to the historical AIS messages provided by the Danish Maritime Authority and ARLAS PROC/ML, an open source and scalable processing platform based on Apache SPARK, we are able to apply our pipeline of processes and extract this information from millions of AIS messages. We use a Hidden Markov Model (HMM) to identify when a vessel is still or moving and we create “courses”, embodying the travel of the vessel. Then we derive the travel indicators. The visualization of results is made possible by ARLAS Exploration, an open source and scalable tool to explore geolocated data. This carto-centered application allows users to navigate into the huge amount of enriched data and helps to take benefits of these new origin and destination indicators. This tool can also be used to help in the creation of Machine Learning algorithms in order to deal with many maritime transportation challenges.

Exploiting multiclass classification algorithms for the prediction of ship routes: a study in the area of Malta

2020 ◽  
Vol 12 (3) ◽  
pp. 289-307
Author(s):  
Angelica Lo Duca ◽  
Andrea Marchetti
Keyword(s):  
Decision Trees ◽  
Nearest Neighbors ◽  
Multiclass Classification ◽  
Machine Learning Algorithms ◽  
Automatic Identification ◽  
Identification System ◽  
Classification Algorithms ◽  
Content Type ◽  
Route Prediction ◽  
Linear Algorithms

Purpose Ship route prediction (SRP) is a quite complicated task, which enables the determination of the next position of a ship after a given period of time, given its current position. This paper aims to describe a study, which compares five families of multiclass classification algorithms to perform SRP. Design/methodology/approach Tested algorithm families include: Naive Bayes (NB), nearest neighbors, decision trees, linear algorithms and extension from binary. A common structure for all the algorithm families was implemented and adapted to the specific case, according to the test to be done. The tests were done on one month of real data extracted from automatic identification system messages, collected around the island of Malta. Findings Experiments show that K-nearest neighbors and decision trees algorithms outperform all the other algorithms. Experiments also demonstrate that linear algorithms and NB have a very poor performance. Research limitations/implications This study is limited to the area surrounding Malta. Thus, findings cannot be generalized to every context. However, the methodology presented is general and can help other researchers in this area to choose appropriate methods for their problems. Practical implications The results of this study can be exploited by applications for maritime surveillance to build decision support systems to monitor and predict ship routes in a given area. For example, to protect the marine environment, the use of SRP techniques could be used to protect areas at risk such as marine protected areas, from illegal fishing. Originality/value The paper proposes a solid methodology to perform tests on SRP, based on a series of important machine learning algorithms for the prediction.

scholarly journals The GEBCO and NOAA Chart Adequacy Workshop

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Cecilia Z. Cortina G. ◽  
Rochelle Wigley ◽  
Shachak Pe'eri
Keyword(s):  
Trinidad And Tobago ◽  
Training Program ◽  
World Wide ◽  
Satellite Images ◽  
Automatic Identification ◽  
Identification System ◽  
Hydrographic Data ◽  
Silver Spring ◽  
Point Data ◽  
Available Information

<p><strong>Abstract.</strong> On July 23, NOAA Coast Survey hosted a three-day Chart Adequacy Workshop that included participants from 13 countries. This is the fourth Chart Adequacy Workshop held at National Oceanic and Atmospheric Administration’s (NOAA) Silver Spring, Maryland campus. This was the fourth workshop hosted by NOAA and Nippon Foundation / General Bathymetric Chart of the Oceans (GEBCO) Training Program at the Center for Coastal and Ocean Mapping, UNH. Unlike previous years (2017, 2016, 2015), the focus of this week was on networking and support for the upcoming International Cartographic Association (ICA) Working Group on Marine Cartography meeting held on July 26 and in preparation for next year’s International Cartographic Conference (ICC).</p><p> The main goal of the workshop is to provide training for professional cartographers and hydrographers on techniques for assessing nautical chart adequacy using publicly-available information, such as satellite images and maritime automatic identification system (AIS) data. . The participants received an overview on Coast Survey datasets, processes, and requirements for nautical charts. They also learned about pre-processing hydrographic data, such as loading charts, uploading imagery, and applying electronic navigation charts (ENCs) and AIS point data. Through a series of lab units, the attendees practiced performing the concepts they learned.</p><p>The 2018 participants were from Australia, Greece, Ireland, Japan, Latvia, Madagascar, Mexico, Nigeria, Peru, Poland, St. Vincent and the Grenadines, Taiwan, and Trinidad and Tobago. The international nature of the event allows the participants to meet and learn from cartographers from a variety of backgrounds and expertises. Thee individuals include Nippon Foundation / GEBCO training program students and those nominated by their home hydrographic offices and their travel was sponsored through funds secured by the workshop organizers.</p><p> The workshop was developed in part too address the need to improve the collection, quality, and availability of hydrographic data world-wide, and increase the standardization of chart adequacy evaluations across the globe. Coast Survey is currently working with the International Hydrographic Organization (IHO) to recommend participants for next year’s workshop towards the end of July, 2019.</p>

scholarly journals Methodology of Acquiring Valid Data by Combining Oil Tankers’ Noon Report and Automatic Identification System Satellite Data

2019 ◽  
Vol 31 (3) ◽  
pp. 299-309 ◽  
Author(s):  
Ali Akbar Safaei ◽  
Hassan Ghassemi ◽  
Mahmoud Ghiasi
Keyword(s):  
Air Pollution ◽  
Fuel Consumption ◽  
Root Mean Square ◽  
Satellite Data ◽  
Expected Value ◽  
Automatic Identification ◽  
Identification System ◽  
Mean Square ◽  
Automatic Identification System ◽  
Valid Data

Fuel consumption of marine vessels plays an important role in both generating air pollution and ship operational expenses where the global environmental concerns toward air pollution and economics of shipping operation are being increased. In order to optimize ship fuel consumption, the fuel consumption prediction for her envisaged voyage is to be known. To predict fuel consumption of a ship, noon report (NR) data are available source to be analysed by different techniques. Because of the possible human error attributed to the method of NR data collection, it involves risk of possible inaccuracy. Therefore, in this study, to acquire pure valid data, the NR raw data of two very large crude carriers (VLCCs) composed with their respective Automatic Identification System (AIS) satellite data. Then, well-known models i.e. K-Mean, Self-Organizing Map (SOM), Outlier Score Base (OSB) and Histogram of Outlier Score Base (HSOB) methods are applied to the collected tankers NR during a year. The new enriched data derived are compared to the raw NR to distinguish the most fitted methodology of accruing pure valid data. Expected value and root mean square methods are applied to evaluate the accuracy of the methodologies. It is concluded that measured expected value and root mean square for HOSB are indicating high coherence with the harmony of the primary NR data.

scholarly journals A modelling system for the exhaust emissions of marine traffic and its application in the Baltic Sea area

2009 ◽  
Vol 9 (23) ◽  
pp. 9209-9223 ◽  
Author(s):  
J.-P. Jalkanen ◽  
A. Brink ◽  
J. Kalli ◽  
H. Pettersson ◽  
J. Kukkonen ◽  
...  
Keyword(s):  
Baltic Sea ◽  
Fuel Consumption ◽  
Exhaust Emissions ◽  
Automatic Identification ◽  
Identification System ◽  
The Baltic Sea ◽  
Support Tool ◽  
Marine Traffic ◽  
The Baltic ◽  
Modelling System

Abstract. A method is presented for the evaluation of the exhaust emissions of marine traffic, based on the messages provided by the Automatic Identification System (AIS), which enable the identification and location determination of ships. The use of the AIS data facilitates the positioning of ship emissions with a high spatial resolution, which is limited only by the inaccuracies of the Global Positioning System (typically a few metres) that is used in vessel navigation. The emissions are computed based on the relationship of the instantaneous speed to the design speed, and the detailed technical information of the engines of the ships. The modelling of emissions is also based on a few basic principles of ship design, including the modelling of the propelling power of each vessel in terms of its speed. We have investigated the effect of waves on the consumption of fuel, and on the emissions to the atmosphere. The predictions of fuel consumption were compared with the actual values obtained from the shipowners. For a Roll on – Roll off cargo/passenger ship (RoPax), the predicted and reported values of annual fuel consumption agreed within an accuracy of 6%. According to the data analysis and model computations, the emissions of NOx, SOx and CO2 originating from ships in the Baltic Sea during the full calendar year of 2007 were in total 400 kt, 138 kt and 19 Mt, respectively. A breakdown of emissions by flag state, the type of ship and the year of construction is also presented. The modelling system can be used as a decision support tool in the case of issues concerning, e.g., the health effects caused by shipping emissions or the construction of emission-based fairway dues systems or emissions trading. The computation of emissions can be automated, which will save resources in constructing emission inventories. Both the methodologies and the emission computation program can be applied in any sea region in the world, provided that the AIS data from that specific region are available.

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