Development of a Vessel-Performance Forecasting System: Methodological Framework and Case Study

Gómez, Rebeca; Camarero, Alberto; Molina, Rafael

doi:10.1061/(asce)ww.1943-5460.0000316

Cited by 12 publications

(7 citation statements)

References 20 publications

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“…Some cases of models based on different ANN techniques for ship motion and/or trajectory prediction in navigation and maneuvering operations can be found in [55][56][57]. Finally, an example of an ANN-based prediction model of loading/unloading productivity in a container ship terminal from some met-ocean and operational input variables is presented in [58].…”

Section: Of 21mentioning

confidence: 99%

A Semi-Supervised Machine Learning Model to Forecast Movements of Moored Vessels

Romano-Moreno

Tomás

Diaz-Hernandez

et al. 2022

JMSE

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The good performance of the port activities in terminals is mainly conditioned by the dynamic response of the moored ship system at a berth. An adequate definition of the highly multivariate processes involved in the response of a moored ship at a berth is crucial for an appropriate characterization of port operability. The availability of an efficient forecast system of the movements of moored ships is essential for the planning, performance, and safety of the development of port operations. In this paper, an inference model to predict moored ship motions, based on a semi-supervised Machine Learning methodology, is presented. A comparison with different supervised and unsupervised Machine Learning techniques, as well as with existing Deep Learning-based models for predicting moored ship motions, has been performed. The highest performance of the semi-supervised Machine Learning-based model has been obtained. Additionally, the influence of infragravity wave parameters introduced as predictor variables in the model has been analyzed and compared with the typical ocean waves, wind, and sea level as predictor variables. The prediction model has been developed and validated with an available dataset of measured data from field campaigns in the Outer Port of Punta Langosteira (A Coruña, Spain).

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Section: Of 21mentioning

confidence: 99%

A Semi-Supervised Machine Learning Model to Forecast Movements of Moored Vessels

Romano-Moreno

Tomás

Diaz-Hernandez

et al. 2022

JMSE

Self Cite

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“…To predict the performance of vessel loading and discharging operations, Gómez et al (2015) propose a neural network that takes into account operational data (e.g., berthing time, number of containers, number of gangs, vessel beam size) as well as wind conditions (e.g., average wind speed, wind direction) during berthing of respective vessels, provided by a Spanish container terminal. By analyzing training errors, the authors highlight the important role of wind conditions.…”

Section: Berth Operationsmentioning

confidence: 99%

From Digitalization to Data-Driven Decision Making in Container Terminals

Heilig¹,

Stahlbock²,

Voß³

2019

Preprint

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With the new opportunities emerging from the current wave of digitalization, terminal planning and management need to be revisited by taking a data-driven perspective. Business analytics, as a practice of extracting insights from operational data, assists in reducing uncertainties using predictions and helps to identify and understand causes of inefficiencies, disruptions, and anomalies in intra-and inter-organizational terminal operations. Despite the growing complexity of data within and around container terminals, a lack of data-driven approaches in the context of container terminals can be identified. In this chapter, the concept of business analytics for supporting terminal planning and management is introduced. The chapter specifically focuses on data mining approaches and provides a comprehensive overview on applications in container terminals and related research. As such, we aim to establish a data-driven perspective on terminal planning and management, complementing the traditional optimization perspective.

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“…The ETA is calculated by a maritime operator using limited sources of information and a very basic estimation technique based on simple arithmetics [11], and it is unable to provide high accuracy [12] or to include other relevant factors beyond the start travel time and distance to port. The estimated ETA is manually inputted into the AIS communication system from where the port obtains the ETA.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of an AIoT Architecture for Introducing a Vessel ETA Cognitive Service in a Legacy Port Management Solution

Valero

Ivancos

Vaño

et al. 2021

Sensors

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Current Internet of Things (IoT) stacks are frequently focused on handling an increasing volume of data that require a sophisticated interpretation through analytics to improve decision making and thus generate business value. In this paper, a cognitive IoT architecture based on FIWARE IoT principles is presented. The architecture incorporates a new cognitive component that enables the incorporation of intelligent services to the FIWARE framework, allowing to modernize IoT infrastructures with Artificial Intelligence (AI) technologies. This allows to extend the effective life of the legacy system, using existing assets and reducing costs. Using the architecture, a cognitive service capable of predicting with high accuracy the vessel port arrival is developed and integrated in a legacy sea traffic management solution. The cognitive service uses automatic identification system (AIS) and maritime oceanographic data to predict time of arrival of ships. The validation has been carried out using the port of Valencia. The results indicate that the incorporation of AI into the legacy system allows to predict the arrival time with higher accuracy, thus improving the efficiency of port operations. Moreover, the architecture is generic, allowing an easy integration of the cognitive services in other domains.

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Development of a Vessel-Performance Forecasting System: Methodological Framework and Case Study

Cited by 12 publications

References 20 publications

A Semi-Supervised Machine Learning Model to Forecast Movements of Moored Vessels

A Semi-Supervised Machine Learning Model to Forecast Movements of Moored Vessels

From Digitalization to Data-Driven Decision Making in Container Terminals

Design and Development of an AIoT Architecture for Introducing a Vessel ETA Cognitive Service in a Legacy Port Management Solution

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