Application of an Analytic Methodology to Estimate the Movements of Moored Vessels Based on Forecast Data

Abstract

A port’s operating capacity and the economic performance of its concessions are intimately related to the quality of its operational conditions. This paper presents an analytical methodology for estimating the movements of a moored vessel based on field measurements and forecast data, specifically including ship dimensions and meteorological and maritime conditions. The methodology was tested and validated in the Outer Port of Punta Langosteira, A Coruña, Spain. It was determined that the significant wave height outside the port, and the ratio of the vessel’s length divided by its beam (L/B), are the variables that most influence movements. Furthermore, heave and surge are the movements with a better value of the coefficient of determination (R2 values of 0.71 and 0.67, respectively), the sway (R2 = 0.30) and roll (R2 = 0.27) being the worst when using the available forecast variables of the Outer Port of Punta Langosteira. Despite their low R2 values, sway and roll models are able to estimate the main trends of these movements. The obtained estimators provide good predictions with assumable error values (root mean square error—RMSE and mean absolute error—MAE), showing their potential application as a predictive tool. Finally, as a consequence, the A Coruña Port Authority has included the results of the methodology in its port management system allowing them to predict moored vessel behavior in the port.

Validation and Application of the Accu-Waves Operational Platform for Wave Forecasts at Ports

“…The goal is to help port managers and ship masters avoid unwanted weather-related ship accidents and vessel bed collisions while mooring, port approaching, ship docking, berth positioning, towing, and harbor dredging procedures take place. Future steps of the Accu-Waves OFP refer to integrating the wave-related danger alerts for large ships with the development of an advisory and forecast service system for safe navigation and mooring in coastal areas per small vessel and fishing boat, too [117][118][119].…”

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

“…Although certain simplifications are also assumed, physical models provide valuable results often used as a complement to numerical modeling to calibrate, validate, and/or refine the numerical results [9,20,25,30,31]. Finally, the third approach is the analysis of the berthed ship system response based on prototype measured data obtained from monitoring campaigns [32][33][34][35][36]. These measured data can be considered the real behavior of the system.…”

“…In recent years, artificial intelligence (AI) techniques have been extensively developed and applied, in multiple fields, to computationally analyze, model, and infer robust performance patterns in different highly monitored physical and engineering processes. Specifically, different cases of AI techniques applied in harbor operability assessment, both in terms of wave agitation [38][39][40][41][42] and moored ship motions [32,33,36], can be found in the literature. Using inference models, relations between variables have been established, allowing conclusions to be deduced from an initial multivariate database.…”

“…The input and output variables are defined to establish the relationship function between the independent variables and the response ones. For example, a linear regressionbased model for the prediction of moored ships' motions from different met-ocean and vessel size input parameters is presented in [32]. In [33], a prediction model for moored ships' responses based on gradient boosting algorithms can be found.…”

Interaction between Waves and Maritime Structures

“…The rationale of Sande et al [38] might be well summarized by the maxim reported at the beginning of the article: "the better the vessels in port, the greater the economic returns". The authors argue that the "degree of comfort" of moored vessels depends to a large extent on the movements they experience during working operations.…”