Wave Prediction Research Articles

JUVENILE DELINQUENCY AND CRIME PREVENTION

Delinquent children are extraordinary children who display significant deviation in their social adjustment and are therefore labeled as socially deviant or socially impaired. They have been discovered to have criminal tendencies and to engage in antisocial behavior. In this regard, they are similar to criminals and antisocial elements. However, in legal parlance, they are referred to as delinquents rather than criminals. The enormous increase in juvenile violence, notably killings, which began in the mid-1980s and peaked in the early 1990s, sparked widespread fear and concern among the public, prompting federal, state, and municipal governments to implement policy adjustments. For example, in response to the rise in juvenile violence and predictions of a new wave of increasingly violent youth, most states tightened their juvenile justice laws, including measures that allow, or in many cases require, children to be transferred to the adult system at younger ages and for a wider range of offences. A considerable body of research has begun to identify characteristics that may raise the risk of adolescent crime. The research has also resulted in the development and evaluation of prevention interventions.

Wind Features Extracted from Weather Simulations for Wind-Wave Prediction Using High-Resolution Neural Networks

Nearshore wave forecasting is susceptible to changes in regional wind fields and environments. However, surface wind field changes are difficult to determine due to the lack of in situ observational data. Therefore, accurate wind and coastal wave forecasts during typhoon periods are necessary. The purpose of this study is to develop artificial intelligence (AI)-based techniques for forecasting wind–wave processes near coastal areas during typhoons. The proposed integrated models employ combined a numerical weather prediction (NWP) model and AI techniques, namely numerical (NUM)-AI-based wind–wave prediction models. This hybrid model comprising VGGNNet and High-Resolution Network (HRNet) was integrated with recurrent-based gated recurrent unit (GRU). Termed mVHR_GRU, this model was constructed using a convolutional layer for extracting features from spatial images with high-to-low resolution and a recurrent GRU model for time series prediction. To investigate the potential of mVHR_GRU for wind–wave prediction, VGGNet, HRNet, and Two-Step Wind-Wave Prediction (TSWP) were selected as benchmark models. The coastal waters in northeast Taiwan were the study area. The length of the forecast horizon was from 1 to 6 h. The mVHR_GRU model outperformed the HR_GRU, VGGNet, and TSWP models according to the error indicators. The coefficient of mVHR_GRU efficiency improved by 13% to 18% and by 13% to 15% at the Longdong and Guishandao buoys, respectively. In addition, in a comparison of the NUM–AI-based model and a numerical model simulating waves nearshore (SWAN), the SWAN model generated greater errors than the NUM–AI-based model. The results of the NUM–AI-based wind–wave prediction model were in favorable accordance with the observed results, indicating the feasibility of the established model in processing spatial data.

A convolutional neural network based model to predict nearshore waves and hydrodynamics

This study proposes a convolutional neural network (CNN) based model to predict waves and hydrodynamics in the nearshore zone. Specifically, we address the so-called next-frame prediction of wave and hydrodynamic conditions using the previous-step conditions. A laboratory experiment on unsteady rip currents is simulated using the non-hydrostatic model SWASH. The simulated wave and hydrodynamic results are divided into two groups. The first group of data is used to train the CNN model, and the second group of data is used to evaluate the trained model by comparing the simulated water surface elevation, cross-shore velocity, and longshore velocity by SWASH with the corresponding prediction by the trained CNN model. The result shows that the CNN model accurately predicts the wave propagation over the offshore slope, wave breaking over the sandbar, and bore propagation over the shore including detailed wave crest bending and separation. Time-averaged water level comparison further confirms that the CNN model captures the wave setup induced by wave breaking. The CNN model also correctly predicts the cross-shore velocity and longshore velocity driven by wave breaking over sandbars and wave–current interaction. Finally, the low-frequency nearshore circulation pattern predicted by the CNN model is also evaluated by time-averaging the velocity field. The comparison shows that the CNN model correctly predicts the distribution of circulation cells and the number of cells, as simulated by the SWASH model. This work serves as a paradigm to integrate traditional coastal modeling with machine learning to study coastal processes.

Surveillance of SARS-CoV-2 in extensive monitoring of municipal wastewater: key issues to yield reliable results.

Several studies have detected SARS-CoV-2 in the stool of infected people as in urban wastewater. The quantification of SARS-CoV-2 in wastewater appears today as a powerful tool that can help in wastewater-based epidemiology (WBE). The goal is to improve the prediction of new waves of COVID-19 outbreaks and provide an early warning of the evolution of the infection. In this research, we highlighted some practical and scientific aspects that emerged during an extensive ongoing monitoring campaign carried out on a large number of wastewater treatment plants located in the province of Trento (North Italy) and aimed at the detection of SARS-CoV-2 in raw municipal wastewater. The open issues underline are related to the collection and storage (sampling protocol, storage and heat treatment), to the molecular analysis (enrichment phase), and to the mathematical calculation of SARS-CoV-2 load in wastewater, suitable for WBE (standard curve to obtain the concentration of genomic units and flow rate measurements). This study provides some insights that can help in the implementation of surveillance plans in other regions.

Rapid phase-resolved prediction of nonlinear dispersive waves using machine learning

The phase-resolved prediction of ocean waves suffers from two major challenges that have so far hindered solutions in real time or faster: (i) The reconstruction of the nonlinear and dispersive waves: how to calculate the state of the wavefield from measurable wave amplitude data, and (ii) Rapid solution: how to integrate equations in a timely manner. Here, we overcome these challenges at once through an advanced machine learning technique based on spatiotemporal patches of the time history of surface wave elevation data in the domain. The proposed time-series analysis uses a new concept of convolution that instead of constructing kernels from filters, implements random patches of spatiotemporal data as the input to predict the next patch. We demonstrate that the proposed methodology can accurately make predictions for ocean surface gravity waves more than two orders of magnitude faster than numerically solving governing equations.

Comparative Analysis on Wave Field Using Wave Measurement and Wave Analysis Data from JMA-MSM

Kim, H.-J.; Cho, W.C., and Moon, Y.-H., 2021. Comparative analysis on wave field using wave measurement and wave analysis data from JMA-MSM. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 181–185. Coconut Creek (Florida), ISSN 0749-0208. Waves are the most important factor in the numerical analysis of coastal hydrodynamics. Wave conditions in coastal areas can be determined in on-site wave measurements or wave numerical analyses. Because the acquisition of spatial wave measurement data in a specific coastal area is limited, the spatial wave data are generally acquired with accurate numerical analyses. In this study, the wave field in a coastal area in South Korea was comparatively analyzed with wave measurements and numerical analyses of wind data acquired with the JMA (Japan Meteorological Agency) Meso-Scale Model (MSM). The high-resolution meteorological data were used for the numerical analysis, and the wave measurement data provided by the Wave Information Network of Korea (WINK) were used for the comparative analysis. Results showed the root mean square errors of 0.3-0.4m for all wave prediction in the East Sea in 2018 and 0.4-0.5m for typhoon KONG-REY in 2018. Therefore, it was concluded that the wave numerical simulations based on the wave data from JMA-MSM overall reproduced the wave field accurately.

Applicability of Disaster Scale Criteria for Swell-Like Waves Based on Wave Overtopping Predictions

Lee, S.-Y.; Kim, D.-H.; Kim, D.-S.; Kim, H.-J.; Jeong, Y.-H.; Hong, S.-J., and Lee, H.-Y., 2021. Applicability of disaster scale criteria for swell-like waves based on wave overtopping predictions. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 226–230. Coconut Creek (Florida), ISSN 0749-0208. In this study, a wave overtopping prediction system was established for 10 major ports adjacent to 10 testbeds in South Korea, and a qualitative assessment of the system's reproducibility was performed through field surveys. The system estimates the probability of wave overtopping hazards, which is required for responding to disasters caused by swell-like waves. Wave overtopping prediction requires information on artificial structures in the major ports near the testbeds, swell-like wave predictions, and wave overtopping rate calculations. The threshold for defining wave overtopping hazards that can affect the hinterland, facilities, and residents in coastal areas was set to 0.01 m3/s/m, in accordance with EurOtop standards (Pullen et al., 2018) and the Korean Ministry of Oceans and Fisheries. The results of this study can be used to facilitate decision-making by providing qualitative estimations of wave overtopping hazards at various levels. By comparing the results to standard values, the risk of swell-like waves in testbeds along the East Sea coast can be determined. The proposed system can be used to avoid casualties and property damage in the event of a wave-overtopping disaster.

Prediction of heat waves using meteorological variables in diverse regions of Iran with advanced machine learning models

Prediction of heat waves using meteorological variables in diverse regions of Iran with advanced machine learning models

GMDH-based wave prediction model for one-week nearshore waves using one-week forecasted global wave data

Wave forecasting up to 168 h (one-week) ahead is crucial to the operational management of shipping and construction in the marine work site. The present study proposes a method of areal-time One-week Wave Forecast of Nearshore Waves (OWFNW) at 13 stations on the Japanese coast. For each station, two Group Method of Data Handling (GMDH) based wave prediction models for the wave height and period are developed to transform the global wave data into nearshore waves. For each GMDH-based wave prediction model, four models are trained with four different training data sets using the one-week global wave forecast data released in real-time by the Japan Meteorological Agency (JMA), National Oceanic and Atmospheric Administration (NOAA), and European center for Medium-Range Weather Forecasts (ECMWF). The study conducts a series of training and testing GMDH-based wave prediction models at each station, and examines the accuracy of predictions. The best performing GMDH-based wave prediction model selected among the four models is compared to the best global wave forecast data. It was found that the best performance GMDH-based wave prediction model varies for different locations. On average, the best performing GMDH-based wave height model is able to improve the accuracy of nearshore wave predictions up to 38% compared to that of the best global wave height forecast with 168 h lead time. The best GMDH-based wave period model improves the prediction error up to 65% compared to the 168 h ahead error of the best global wave period forecasts. The present methodology to get OWFNW can be applied to other nearshore seas where global wave forecast data and corresponding observed one are available.

Sensitivity of low‐tropospheric Arctic temperatures to assimilation of AIRS cloud‐cleared radiances: Impact on midlatitude waves

AbstractIn this work it is shown that the prediction of individual midlatitude waves in a global forecast framework may benefit from the assimilation of cloud‐cleared radiances (CCRs) from the Atmospheric Infrared Sounder (AIRS). The assimilation of CCRs, relative to clear‐sky radiances, provides more information in high‐latitude areas affected by broken low‐level stratus clouds, which are common over north polar latitudes during the summer and autumn seasons. This added information in cloudy regions produces slightly lower low‐level temperatures and lower mid‐tropospheric heights (through hydrostatic adjustment) over the Arctic and part of northeastern Siberia. In areas that are both dynamically unstable (with the potential for rapid error growth) and data void (as observed by clear‐sky radiances), the assimilation of CCRs provides valuable information that can improve the representation of individual baroclinic waves and their subsequent forecasts. The assimilation of AIRS CCRs slightly modifies the geopotential height gradients between the Arctic and the midlatitudes, leading to improvements in the forecast of individual baroclinic waves, as is shown through three case‐studies. The set of observing system experiments (OSEs) is performed with the NASA Goddard Earth Observing System (GEOS) data assimilation and forecast system during boreal autumn 2014. AIRS CCRs are thinned to approximately one quarter the density of operationally assimilated AIRS radiances, consistent with their higher information content. Global 6‐hourly analyses are produced from 01 September to 10 November 2014 and seven‐day forecasts are initialized at 0000 UTC daily. Since the CCR methodology is widely applicable, these findings are also relevant to other infrared sensors.

Artificial Intelligence - Technology for Prediction and Prevention of third Wave of COVID-19 Pandemic

Pandemics bear unique challenges which require a fast response from health system on many aspects ranging from prevention to management through rapid diagnostic modalities, drug/vaccine discovery, and health resource allocation and planning management. However, in low resource settings, the mismatch between demand & supply of health services and inadequate knowledge about the course of pandemic results in failure in management of pandemic & resulted in loss of human lives. However, due to discovery of the latest technologies like artificial intelligence (AI), the pandemic is often managed from prevention to management level in a predictive manner. AI is increasingly being studied as a useful gizmo to assist in preventing pandemic and managing existing crisis in a timely manner. However, while AI has been proved to be useful in its ability to assist halting the rapid spread or contamination of disease during a pandemic, there exist few ethical and legal issues that have gotten to be taken care of before it is to be utilized in a mass scale.

Performance evaluation of SWAN ST6 physics forced by ERA5 wind fields for wave prediction in an enclosed basin

In this study, the performance of SWAN (Simulating WAves Nearshore) spectral wave model ST6 physics forced by ERA5 reanalysis wind data were evaluated in predicting the wave properties in an enclosed basin, the Black Sea. ERA5 wind data were validated against in-situ measurements. Although two data match quite well, a 10–20% underestimation in wind speeds and velocity components was found. Spectral wave modeling study was conducted in two phases. In the first phase, 16 model setup combinations were generated using default parameterizations of SWAN spectral wave model with different physics (Westhuysen, Komen, Janssen, ST6) and with different wind sources (ERA5, ERA-Interim, and CFSv2). The model setups forced by ERA5 winds had the highest correlation values and in combination with ST6 physics performed better than the others in the prediction of wave heights. At the next stage, this model setup was calibrated at two stations by conducting 82 different parameterizations. Chosen model parameterization with overall satisfying performance was validated at five buoys. Comparisons among similar runtime model runs with timesteps up to 60 min and iteration steps up to four, show that higher timesteps with higher iterations give better results than lower time steps with one iteration. Debias and wind scaling parameters were found to be the most significant at adjusting wave heights. Comparison with previous calibration studies indicated that model runs based on calibrated ST6 physics parameterization and forced by ERA5 winds provided an increased accuracy of wave height predictions in the Black Sea.

The performance and optimization of ANN-WP model under unknown sea states

Ocean wave is one of the main factors to cause the 6 degrees of freedom motion of ships. Accurate wave surface prediction is paramount to ship safety. The artificial neural network-based wave prediction (ANN-WP) model is a phase-resolved wave prediction model based on artificial intelligence, which can provide high-precision prediction results of wave surface. However, it needs training with a large dataset to make the model obtain a satisfying prediction performance. When unknown sea states (not included in the training data) appear in practice, the reliability of the prediction results is low. In this study, the performance of the ANN-WP model under unknown sea states is investigated based on wave tank experiment. The data distribution of training data set is considered as one of the factors that affects the performance of ANN-WP model under unknown sea states. Based on this consideration, data mixing is employed to improve the performance of the ANN-WP model under unknown sea states. Through this method, the prediction error of the model for unknown sea states decreased by approximately 5.0%–10.0%.

A deep learning approach based on a data-driven tool for classification and prediction of thermoelastic wave’s band structures for phononic crystals

This article deals with the data-driven prediction of the band structures of thermoelastic waves in the nano-scale phononic crystal beams considering nano-size effects. The computational intelligence methods are developed using a data-driven tool to discover the design space of phononic crystals, which are subjected to thermal shock loading. For this purpose, a rich dataset is created utilizing an analytical solution, which was previously proposed for the nonlocal coupled thermoelasticity analysis in a nano-sized phononic crystal beam. The preprocessing methods, hyperparameters optimization, and shallow and deep neural networks are used to classify and predict the bandgaps. Also, according to the created dataset and the data-driven method, the phononic crystal feature importance and behavior based on the design parameters are assessed in detail. The detailed investigation reveals the importance of the design parameters according to the deep neural network’s results. It is demonstrated by numerical results that the proposed final data-driven model can predict the characteristics of the phononic crystals pretty well. Also, the results show that the deep neural network outperforms the shallow neural network for the classification and prediction of the band structures. The final results can be a helpful tool to have a fast numerical framework for the prediction of photonic crystals’ bandgaps before the application of time-consuming accurate frameworks.

Solitary matter wave in spin-orbit-coupled Bose-Einstein condensates with helicoidal gauge potential.

We analytically and numerically study the different types of solitary wave in the two-component helicoidal spin-orbit coupled Bose-Einstein condensates (BECs). Adopting the multiscale perturbation method, we derive the analytical bright and dark solitary wave solutions of the system, and the stationary and moving bright (dark) solitary waves are obtained. The effects of spin-orbit coupling, the helicoidal gauge potential, the momentum, the Zeeman splitting, and the atomic interactions on the solitary wave types are discussed, and it is found that the coupling of these physical parameters can manipulate different types of solitary waves in the system. The results indicate that the helicoidal gauge potential breaks the symmetric properties of the energy band of the system and adjusts the energy band structure, thus further effecting the solitary wave properties, i.e., stationary or moving solitary wave, bright, or dark solitary wave. Correspondingly, the analytical predictions for exciting stationary or moving bright (dark) solitary wave in parameter space are obtained. In particular, the helicoidal gauge potential changes the solitary wave types drastically for the weak spin-orbit coupling, i.e., in the absence of the helicoidal gauge potential, only dark (bright) solitary wave solutions exist in the system with repulsive (attractive) atomic interaction; however, in the presence of the helicoidal gauge potential, both dark and bright solitary waves can exist in the system regardless of whether the atomic interaction is repulsive or attractive. In addition, we investigate the stability of solitary waves and obtain the stability regions of different types of solitary waves by applying the linear stability analysis. The dynamic evolution results of the solitary waves by the direct numerical simulation not only validate the linear stability analysis but also confirm the analytical prediction of the solitary waves. Finally, the collision effects between solitary waves are also presented by the numerical simulation. It is shown that the interactions between solitary waves in the system have both elastic and inelastic collisions, which are closely related to the position of solitary wave states in the linear energy band. Our results provide a potential way to adjust the types of solitary waves in BECs with helicoidal gauge potential.

Performance Accuracy of Surfbeat in Modeling Infragravity Waves near and Inside a Harbor

Infragravity (IG) waves significantly affect the operational efficiency of ports. Therefore, an accurate prediction of IG waves inside a harbor is necessary. In this study, the accuracy of the wave-group-resolving model XBeach Surfbeat (XB-SB, Delft University of Technology, Delft, The Netherlands) in predicting the IG waves inside a harbor was assessed by comparing its results with field measurements. Field measurements were performed at Hambantota Port in southern Sri Lanka. Three acoustic waves and current sensors were used to observe the wave characteristics inside and outside the harbor. First, the model was validated against observations outside the port. Next, the performance accuracy of XB-SB in modeling the hydrodynamics in the harbor was evaluated by comparing its results with the values measured inside the port. The results of the numerical simulations indicated that both the nearshore short and IG wave heights can be accurately reproduced by XB-SB in an open domain without many obstacles. However, the short wave heights in the harbor are severely underestimated by XB-SB. The IG waves inside the harbor are overestimated most of the time. Moreover, the natural periods of Hambantota Port are well calculated by XB-SB. In general, XB-SB is a reliable tool for predicting nearshore IG waves. However, it requires further improvement to reproduce the hydrodynamics in a well-sheltered harbor, such as Hambantota Port.

Forecasting wind waves in the US Atlantic Coast using an artificial neural network model: Towards an AI-based storm forecast system

This study presents an artificial intelligence (AI) model to forecast time-series wind waves in the US Atlantic Coast. The fundamental technique in the proposed model is the Long Short-term Memory model, which is able to learn patterns from data sequence. The model is trained using historical wind data, wave data, temperature data, and atmospheric pressure data. Then the model is used to forecast significant wave height, average wave period, and mean wave direction. A 2-year meteorological data measured at four NOAA buoy stations in the US Atlantic Coast are used to train and evaluate the model’s forecast skills. The results show the artificial neural network model converges fast and does not have over-fitting nor under-fitting issues. Furthermore, a short-term forecast (e.g., 1 to 6 h) achieves higher accuracy than a long-term forecast (e.g., 24 to 48 h). This study shows that sufficient forecast accuracy can be obtained by using the input hours equal to the forecast lead time. Comparison using rose plots confirms that the artificial neural network model reproduces wave height and wave period statistics very well. The artificial neural network model is used to forecast storm waves induced by Hurricanes Isaias and Eta in the US South Atlantic region, and two winter storms in the US North Atlantic region. The model forecast is compared with NOAA measurement. The results show that the artificial neural network model is able to accurately forecast both significant wave height and average wave period associated with storm events when the forecast lead time is relatively short (e.g., 1 to 6 h). The proposed artificial neural network model can serve as an alternative tool to traditional coastal models for wave prediction and storm forecast.

Prediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods

Abstract The accurate prediction of the mean wave overtopping rate at breakwaters is vital for a safe design. Hence, providing a robust tool as a preliminary estimator can be useful for practitioners. Recently, soft computing tools such as artificial neural networks (ANN) have been developed as alternatives to traditional overtopping formulae. The goal of this paper is to assess the capabilities of two kernel-based methods, namely Gaussian process regression (GPR) and support vector regression for the prediction of mean wave overtopping rate at sloped breakwaters. An extensive dataset taken from the EurOtop database, including rubble mound structures with permeable core, straight slopes, without berm, and crown wall, was employed to develop the models. Different combinations of the important dimensionless parameters representing structural features and wave conditions were tested based on the sensitivity analysis for developing the models. The obtained results were compared with those of the ANN model and the existing empirical formulae. The modified Taylor diagram was used to compare the models graphically. The results showed the superiority of kernel-based models, especially the GPR model over the ANN model and empirical formulae. In addition, the optimal input combination was introduced based on accuracy and the number of input parameters criteria. Finally, the physical consistencies of developed models were investigated, the results of which demonstrated the reliability of kernel-based models in terms of delivering physics of overtopping phenomenon.

Research on the Blade Motion of a Bidirectional Energy-Generating Turbine under Integrated Wave and Tidal Current Action

An integrated wave-tidal current power turbine is affected by both wave and tidal current forces, and its energy efficiency is closely related to the velocity and direction of the two forces. To improve the probability of the horizontal axis turbine reaching maximum energy efficiency under real-time changing sea conditions, we performed the following investigations in this study. Based on the actual application scenario of Lianyungang port, a time series prediction model of tidal current (velocity and flow direction) and wave (mean wave direction, mean wave period, and significant wave height) data for the past year was established. The changes in waves and tidal currents within 24 h after the cutoff point of the existing data were predicted. The integrated wave-tidal current mechanism was studied, and the superposition of wave energy and tidal current energy was transformed into the equivalent velocity vector of wave-tidal current integration. The conversion coefficient between waves and equivalent flows was determined by a numerical wave flume simulation. According to the historical wave and tidal current data, the equivalent velocity range of the integrated action of waves and tidal currents in Lianyungang was determined. The influence of different blade motions on the energy harvesting efficiency of the turbine under the corresponding flow conditions was studied using the Computational Fluid Dynamics (CFD) method to determine the blade motion law of the turbine. The blade motion law of the prototype was verified in a sea trial experiment. The experimental results were basically consistent with the simulation results for the blade motion law designed according to the wave and tidal current prediction law. This design scheme can provide a reference for engineering design for the development and utilization of new marine energy.

Optimization of SWAN Wave Model to Improve the Accuracy of Winter Storm Wave Prediction in the East Sea

In recent years, as human casualties and property damage caused by hazardous waves have increased in the East Sea, precise wave prediction skills have become necessary. In this study, the Simulating WAves Nearshore (SWAN) third-generation numerical wave model was calibrated and optimized to enhance the accuracy of winter storm wave prediction in the East Sea. We used Source Term 6 (ST6) and physical observations from a large-scale experiment conducted in Australia and compared its results to Komen’s formula, a default in SWAN. As input wind data, we used Korean Meteorological Agency's (KMA’s) operational meteorological model called Regional Data Assimilation and Prediction System (RDAPS), the European Centre for Medium Range Weather Forecasts’ newest 5th generation re-analysis data (ERA5), and Japanese Meteorological Agency's (JMA’s) meso-scale forecasting data. We analyzed the accuracy of each model’s results by comparing them to observation data. For quantitative analysis and assessment, the observed wave data for 6 locations from KMA and Korea Hydrographic and Oceanographic Agency (KHOA) were used, and statistical analysis was conducted to assess model accuracy. As a result, ST6 models had a smaller root mean square error and higher correlation coefficient than the default model in significant wave height prediction. However, for peak wave period simulation, the results were incoherent among each model and location. In simulations with different wind data, the simulation using ERA5 for input wind datashowed the most accurate results overall but underestimated the wave height in predicting high wave events compared to the simulation using RDAPS and JMA meso-scale model. In addition, it showed that the spatial resolution of wind plays a more significant role in predicting high wave events. Nevertheless, the numerical model optimized in this study highlighted some limitations in predicting high waves that rise rapidly in time caused by meteorological events. This suggests that further research is necessary to enhance the accuracy of wave prediction in various climate conditions, such as extreme weather.