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1.
This paper presents the development of a Regional Neural Network for Water Level (RNN_WL) predictions, with an application to the coastal inlets along the South Shore of Long Island, New York. Long-term water level data at coastal inlets are important for studying coastal hydrodynamics sediment transport. However, it is quite common that long-term water level observations may be not available, due to the high cost of field data monitoring. Fortunately, the US National Oceanographic and Atmospheric Administration (NOAA) has a national network of water level monitoring stations distributed in regional scale that has been operating for several decades. Therefore, it is valuable and cost effective for a coastal engineering study to establish the relationship between water levels at a local station and a NOAA station in the region. Due to the changes of phase and amplitude of water levels over the regional coastal line, it is often difficult to obtain good linear regression relationship between water levels from two different stations. Using neural network offers an effective approach to correlate the non-linear input and output of water levels by recognizing the historic patterns between them. In this study, the RNN_WL model was developed to enable coastal engineers to predict long-term water levels in a coastal inlet, based on the input of data in a remote NOAA station in the region. The RNN_WL model was developed using a feed-forwards, back-propagation neural network structure with an optimized training algorithm. The RNN_WL model can be trained and verified using two independent data sets of hourly water levels.The RNN_WL model was tested in an application to Long Island South Shore. Located about 60–100 km away from the inlets there are two permanent long-term water level stations, which have been operated by NOAA since the1940s. The neural network model was trained using hourly data over a one-month period and validated for another one-month period. The model was then tested over year-long periods. Results indicate that, despite significant changes in the amplitudes and phases of the water levels over the regional study area, the RNN_WL model provides very good long-term predictions of both tidal and non-tidal water levels at the regional coastal inlets. In order to examine the effects of distance on the RNN_WL model performance, the model was also tested using water levels from other remote NOAA stations located at longer distances, which range from 234 km to 591 km away from the local station at the inlets. The satisfactory results indicate that the RNN_WL model is able to supplement long-term historical water level data at the coastal inlets based on the available data at remote NOAA stations in the coastal region. 相似文献
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Marcel R.A. van Gent Henk F.P. van den Boogaard Beatriz Pozueta Josep R. Medina 《Coastal Engineering》2007
A method has been developed to estimate wave overtopping discharges for a wide range of coastal structures. The prediction method is based on Neural Network modelling. For this purpose use is made of a data set obtained from a large number of physical model tests (collected within the framework of the European project CLASH, see e.g. [Steendam, G.J., Van der Meer, J.W., Verhaeghe, H., Besley, P., Franco, L. and Van Gent, M.R.A. (2004). The international database on wave overtopping. World Scientific, Proc. 29th ICCE, vol. 4, pp. 4301–4313, Lisbon, Portugal.]). Moreover, a method was developed to obtain confidence intervals for the overtopping predictions of the neural network. 相似文献
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1 .Introduction Large civil engineering structures are exposed to various external loads such as earthquakes ,winds ,traffic and wave loads during their lifetime . The structures may become deteriorated and de-graded withtime in an unexpected way, which m… 相似文献
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Headland-bay beaches are a typical feature of many of the world's coastlines. Their curved planform has aroused much interest since the early days of Coastal Engineering. Modelling this characteristic planform is a task of great interest, not least in relation to projects of coastal structures whose effects on the shoreline must be studied from the planning stages. In this work, Artificial Intelligence is applied to this task—in particular, artificial neural networks (ANNs). Unlike conventional planform models, they are not based on a given mathematical expression of the shoreline curve. Instead, they learn from experience (from a number of training cases) how the planform of a headland-bay beach is shaped, with due regard to the obliquity of incident waves. Three artificial neural networks, with different input/output structures, are implemented and subsequently trained with a number of bays. Once trained, they are tested for validation on other headland-bay beaches. Finally, the most performing neural network is compared with a state-of-the-art planform model. 相似文献
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With the support of big data and GPU acceleration training, the artificial intelligence technology with deep learning as its core is developing rapidly and has been widely used in many fields. At the same time, feature extraction operations are required by the current image-based corrosion damage detection method in the field of ships, with little effect but consuming the large amount of manpower and financial resources. Therefore, a new method for hull structural plate corrosion damage detection and recognition based on artificial intelligence using convolutional neural network is proposed. The convolutional neural network (CNN) model is trained through a large number of classified corrosion damage images to obtain a classifier model. Then the classifier model is used with overlap-scanning sliding window algorithm to recognize and position the location of corrosion damage. Finally, the damage detection pattern for hull structural plate corrosion damage as well as other types of superficial structural damage using convolutional neural network is proposed, which can accelerate the application of artificial intelligence technology into the field of naval architecture & ocean engineering. 相似文献
6.
Forecast of storm surge by means of artificial neural network 总被引：1，自引：0，他引：1
Marzenna Sztobryn 《Journal of Sea Research》2003,49(4):317
This study describes the construction and verification of a model of sea level changes during a storm surge, applying artificial neural network (ANN) methodology in hydrological forecasting in a tideless sea where the variation of water level is only wind generated. Some neural networks were tested to create the forecast model. The results of ANN were compared with observed sea-level values, and with the forecasts calculated by different routine methods. The results of verification show that the neural network methodology could be successfully applied in the routine, operational forecast service. 相似文献
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The performance of an oscillating water column (OWC) wave energy converter depends on many factors, such as the wave conditions, the tidal level and the coupling between the chamber and the air turbine. So far most studies have focused on either the chamber or the turbine, and in some cases the influence of the tidal level has not been dealt with properly. In this work a novel approach is presented that takes into account all these factors. Its objective is to develop a virtual laboratory which enables to determine the pneumatic efficiency of a given OWC working under specific conditions of incident waves (wave height and period), tidal level and turbine damping. The pneumatic efficiency, or efficiency of the OWC chamber, is quantified by means of the capture factor, i.e. the ratio between the absorbed pneumatic power and the available wave energy. The approach is based on artificial intelligence—in particular, artificial neural networks (ANNs). The neural network architecture is chosen through a comparative study involving 18 options. The ANN model is trained and, eventually, validated based on an extensive campaign of physical model tests carried out under different wave conditions, tidal levels and values of the damping coefficient, representing turbines of different specifications. The results show excellent agreement between the ANN model and the experimental campaign. In conclusion, the new model constitutes a virtual laboratory that enables to determine the capture factor of an OWC under given wave conditions, tidal levels and values of turbine damping, at a lower cost and in less time than would be required for conventional laboratory tests. 相似文献
9.
Accessible high-quality observation datasets and proper modeling process are critically required to accurately predict sea level rise in coastal areas. This study focuses on developing and validating a combined least squares-neural network approach applicable to the short-term prediction of sea level variations in the Yellow Sea, where the periodic terms and linear trend of sea level change are fitted and extrapolated using the least squares model, while the prediction of the residual terms is performed by several different types of artificial neural networks. The input and output data used are the sea level anomalies (SLA) time series in the Yellow Sea from 1993 to 2016 derived from ERS-1/2, Topex/Poseidon, Jason-1/2, and Envisat satellite altimetry missions. Tests of different neural network architectures and learning algorithms are performed to assess their applicability for predicting the residuals of SLA time series. Different neural networks satisfactorily provide reliable results and the root mean square errors of the predictions from the proposed combined approach are less than 2?cm and correlation coefficients between the observed and predicted SLA are up to 0.87. Results prove the reliability of the combined least squares-neural network approach on the short-term prediction of sea level variability close to the coast. 相似文献
10.
Application of artificial neural networks in tide-forecasting 总被引：3，自引：0，他引：3
An accurate tidal forecast is an important task in determining constructions and human activities in ocean environments. Conventional tidal forecasting has been based on harmonic analysis using the least squares method to determine harmonic parameters. However, a large number of parameters are required for the prediction of a long-term tidal level with harmonic analysis. Unlike conventional harmonic analysis, this paper presents an artificial neural network (ANN) model for forecasting the tidal-level using the short term measuring data. The ANN model can easily decide the unknown parameters by learning the input–output interrelation of the short-term tidal records. Three field data with three types of tides will be used to test the performance of the proposed ANN model. The numerical results indicate that the hourly tidal levels over a long duration can be predicted using a short-term hourly tidal record. 相似文献
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In the last few decades, considerable efforts have been devoted to the phenomenon of wave-induced liquefactions, because it is one of the most important factors for analysing the seabed and designing marine structures. Although numerous studies of wave-induced liquefaction have been carried out, comparatively little is known about the impact of liquefaction on marine structures. Furthermore, most previous researches have focused on complicated mathematical theories and some laboratory work. In the present study, a data dependent approach for the prediction of the wave-induced liquefaction depth in a porous seabed is proposed, based on a multi-artificial neural network (MANN) method. Numerical results indicate that the MANN model can provide an accurate prediction of the wave-induced maximum liquefaction depth with 10% of the original database. This study demonstrates the capacity of the proposed MANN model and provides coastal engineers with another effective tool to analyse the stability of the marine sediment. 相似文献
12.
基于多种神经网络的风暴潮增水预测方法的比较分析 总被引：1，自引：0，他引：1
简要介绍了利用BP神经网络、小波神经网络、递归神经网络进行风暴潮增水值预测的原理。选取广东省珠江口以南的阳江站2017年风暴潮增水数据进行测试。结果表明,三种神经网络方法针对阳江地区风暴潮增水的预测均具有可靠性和实用性。以当前增水值为输入量的单因子模型更能反映真实风暴潮增水趋势,而从增水极值预测的准确性来看,以台风风力、气压、风向等相关参数为输入量的多因子模型优于单因子模型。BP神经网络更适用于多因子长时间预测,小波神经网络在单因子短时间预测上准确性更高,递归神经网络预测值与实测值相关性更强。在工程运用中,需根据地域时空特点、数据资料的丰富度与预测值评估指标选择合适的方法。 相似文献
13.
Propulsion system with flexible/rigid oscillating fin 总被引：1，自引：0，他引：1
Yamamoto I. Terada Y. Nagamatu T. Imaizumi Y. 《Oceanic Engineering, IEEE Journal of》1995,20(1):23-30
The purpose of this paper is to describe the feasibility research on an oscillating fin propulsion control system as a vehicle actuator. The system is designed and constructed in order to be combined with ship models. Tank cruising tests are conducted to confirm the system's feasibility. As a result, several advantages of the oscillating fin system are found. A neural network is successfully applied for an identification of the ship model with the oscillating fin, and its effectiveness is confirmed 相似文献
14.
This paper describes a major programme of small-scale physical model tests to establish better the influence of armour type and configuration on overtopping. Specifically, 179 tests determined the relative difference in overtopping behaviour for 13 types/configurations of armour. Roughness factors γf were determined for rock (two layers), cubes (single layer and two layers), Tetrapod, Antifer, Haro, Accropode, Core-Loc™ and Xbloc™. These roughness influence factors have been included in the CLASH database and are for use in the neural network prediction of overtopping. Individual wave-by-wave overtopping volumes were analysed and found to compare well with current prediction methods. Measured reflection coefficients for the different units are also presented and compared with recent formulae. 相似文献
15.
水库浮游植物群落动态的人工神经网络方法 总被引：5，自引：0，他引：5
根据辽宁大伙房水库1980－1997年的水文和湖沼学观测资料，分别建立浮游植物丰度和蓝藻优势度人工视经网络模型，将年降雨量，7－9月平均水温，7－8月入库水量与7－8月库容之比和磷酸盐作为输入，浮游植物量和丰度作为输出，建立浮游植物落消长的人工神经网络模型，将7－9月平均水温，7－8月入出库水量之比，磷酸盐和总氨作为输入，蓝藻优势度作为输出，建立浮游植物演替的人工神经网络预测模型，并进行检验，其模拟值与观测值平均相对误差分别2％和1％，结果表明，人工神经网络方法优于传统的统计学模型，可进行水库浮游植物群落动态的预测预报，并具有较高的精度。 相似文献
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The application of a neural network controller to remotely operated vehicles (ROVs) is described. Three learning algorithms for online implementation of a neural net controller are discussed with a critic equation. These control schemes do not require any information about the system dynamics except an estimate of the inertia terms. Selection of the number of layers in the neural network, the number of neurons in the hidden layer, initial weights for the network and the critic coefficient were done based on the results of preliminary tests. The performances of the three learning algorithms were compared by computer simulation. The effectiveness of the neural net controller in handling time-varying parameters and random noise is shown by a case study of the ROV system 相似文献
18.
1 .IntroductionTheartificialneuralnetwork(ANN)hasbeenwidelyusedinmanyscientificfieldsinrecentyears .Itisakindofinformationmanagementsystemthatresemblesthehumanbraininworkpattern .Comparedwiththetraditionalmethodsofnumericalsimulation ,ANNhastheadvantagesofrelativein dependenceofphysicalmodel,uniformandsimplewayofrealization ,quicknessofcomputing ,andsoon .Sincethemodelofartificialneuronswasfirstlyintroducedin 1 943,ithasbeendevelopedthroughseveralstages.TheapplicationofANNhadnotbeenpopular… 相似文献
19.
The flocculation of cohesive sediment in the presence of waves is investigated using high-resolution field observations and a newly-developed flocculation model based on artificial neural networks. Vertical profiles of suspended sediment concentration and turbulent intensity are estimated using measurements of current profile and acoustic backscatter. The vertical distribution of floc size is estimated using an artificial neural network (ANN) that is trained and validated using floc size measurements at one vertical level. Data analysis suggests a linear correlation between suspended sediment concentration and turbulence intensity. Observations and numerical simulations show that floc size is inversely related to sediment concentration, turbulence intensity and water temperature. The numerical results indicate that floc growth is supported by low concentration and low turbulence. In the vertical direction, mean size of flocs decreases toward the bottom, suggesting floc breakage due to increasing turbulence intensity toward the bed. A significant decrease in turbulent shear could occur within the bottom few-cm, related to increased damping of turbulence by sediment induced density stratification. The results of the numerical simulations presented here are consistent with the concept of a cohesive sediment particle undergoing aggregation-fragmentation processes, and suggest that the ANN can be a precise tool to study flocculation processes. 相似文献