Operational Probabilistic Forecasting of Coastal Freak Waves by Using an Artificial Neural Network

Abstract

Coastal freak waves (CFWs) are unpredictable large waves that occur suddenly in coastal areas and have been reported to cause casualties worldwide. CFW forecasting is difficult because the complex mechanisms that cause CFWs are not well understood. This study proposes a probabilistic CFW forecasting model that is an advance on the basis of a previously proposed deterministic CFW forecasting model. This study also develops a probabilistic forecasting scheme to make an artificial neural network model achieve the probabilistic CFW forecasting. Eight wave and meteorological variables that are physically related to CFW occurrence were used as the inputs for the artificial neural network model. Two forecasting models were developed for these inputs. Model I adopted buoy observations, whereas Model II used wave model simulation data. CFW accidents in the coastal areas of northeast Taiwan were used to calibrate and validate the model. The probabilistic CFW forecasting model can perform predictions every 6 h with lead times of 12 and 24 h. The validation results demonstrated that Model I outperformed Model II regarding accuracy and recall. In 2018, the developed CFW forecasting models were investigated in operational mode in the Operational Forecast System of the Taiwan Central Weather Bureau. Comparing the probabilistic forecasting results with swell information and actual CFW occurrences demonstrated the effectiveness of the proposed probabilistic CFW forecasting model.

Is the Spatial-Temporal Dependence Model Reliable for the Short-Term Freight Volume Forecast of Inland Ports? A Case Study of the Yangtze River, China

“…Machine learning (ML) algorithms can handle an extensive amount of data without any constraint on the degree of complexity compared to conventional statistical techniques [17,38,39], and they can capture the underlying mechanism that governs that data [40]. ANN (Artificial Neural Network) is the most popular ML method for predicting freight volume.…”

Storm Tide and Wave Simulations and Assessment

“…Doong et al [1] propose a probabilistic coastal freak wave (CFW) forecasting model that is an advancement of a previously proposed deterministic CFW forecasting model. They also develop a probabilistic forecasting scheme to make an artificial neural network (ANN) model achieve probabilistic CFW forecasting.…”

“…To improve and enhance our simulating and analytic capabilities and understanding of storm tides, surges, and waves, this Special Issue is intended to collect the latest studies or reviews on storm tides, surges, and waves modeling and analysis utilizing dynamic and statistical models and artificial intelligence approaches. Seven high-quality papers have been published in this Special Issue which cover the application and development of many high-end techniques for storm tides, surges, and waves: for instance, employment of an artificial neural network for predicting coastal freak waves [1]; reproduction of super typhoon-created extreme waves [2]; numerical analysis of nonlinear interactions for storm waves, tides, and currents [3]; wave simulation for an island using a circulationwave coupled model [4]; analysis of typhoon-induced waves along typhoon tracks in the western North Pacific Ocean [5]; understanding of how a storm surge prevents or severely restricts aeolian supply [6]; and investigation of coastal settlements and assessment of their vulnerability [7].…”

Machine Learning-Based Models for Accident Prediction at a Korean Container Port

“…In the maritime field, several researchers have examined safety by predicting vessel accidents on the waterway [9], forecasting coastal waves [10], and examining ship collisions [11,12], among other aspects. Moreover, although several researchers have implemented risk assessment methods to identify the risk factors associated with container ports [4][5][6], research regarding the prediction of container port accidents remains limited.…”

“…In the field of maritime and port logistics, machine learning methods have been applied in certain studies to predict the risk associated with accidents. Several researchers have applied neural networks to predict vessel accidents on the waterway [9] and risk in maritime safety [23] and forecast coastal waves [10]. A neural network model was applied to develop a ship collision risk model [11].…”