共查询到20条相似文献，搜索用时 93 毫秒
1.
《Coastal Engineering》2007,54(9):643-656
This paper aims at improving the prediction of wave transmission behind low-crested breakwaters by means of a numerical model based on Artificial Neural Networks (ANNs). The data here used are those gathered within the European research project DELOS.Firstly, the motivations that lead to employ an ANN numerical model to forecast the wave transmission behind low-crested structures are discussed. Then, the ANN model is tested and its architecture is optimized with a test targeted on assessing both the accuracy and the robustness of the method. A study is devoted to investigate the ANN model capability in reproducing some physical relationships among the involved parameters. Finally, comparisons of ANN results with those from experimental formulations based on the classic regression approach demonstrate a considerable improvement in the forecast accuracy.The ANN forecasting tool is available as a user-friendly Internet applet at: http://w3.uniroma1.it/cmar/wave_transm_kt.htm. 相似文献
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An artificial neural network (ANN) was applied to predict seasonal beach profile evolution at various locations along the Tremadoc Bay, eastern Irish Sea. The beach profile variations in 19 stations for a period of about 7 years were studied using ANN. The model results were compared with field data. The most critical part of constructing ANN was the selection of minimum effective input data and the choice of proper activation function. Accordingly, some numerical techniques such as principal component analysis and correlation analysis were employed to detect the proper dataset. The geometric properties of the beach, wind data, local wave climate, and the corresponding beach level changes were fed to a feedforward backpropagation ANN. The performance of less than 0.0007 (mean square error) was achieved. The trained ANN model results had very good agreement with the beach profile surveys for the test data. Results of this study show that ANN can predict seasonal beach profile changes effectively, and the ANN results are generally more accurate when compared with computationally expensive mathematical model of the same study region. The ANN model results can be improved by the addition of more data, but the applicability of this method is limited to the range of the training data. 相似文献
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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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By coupling numerical wave model (NWM) and artificial neural networks (ANNs), a new procedure for wave prediction is proposed. In many situations, numerical wave modeling is not justified due to economical consideration. Although incorporation of an ANN model is inexpensive, such a model needs a long time period of wave data for training, which is generally inconvenient to achieve. A proper combination of these two methods could carry the potentials of both. Based on the proposed approach, wave data are generated by a NWM by means of a short period of assumed winds at a concerned point. Then, an ANN is designed and trained using the above-mentioned generated wind-wave data. This ANN model is capable of mapping wind-velocity time series to wave height and period time series with low cost and acceptable accuracy. The method was applied for wave hindcasting to two different sites; Lake Superior and the Pacific Ocean. Simulation results show the superiority of the proposed approach. 相似文献
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The main objective of the present study is to develop a new two-phase procedure in order to localize the faults and corresponding severity in thin plate structures. Initially, the variation of modal flexibility and load-deflection differential equation of plate in conjunction with the invariant expression for the sum of transverse load are employed to formulate the damage indicator. Then an Artificial Neural Network (ANN) techniques and genetic algorithm are implemented to determine the corresponding damage severity. Genetic algorithm (GA) is used to automate the parameter selection process in artificial neural networks and eliminate the context dependent notion of the ANNs. The feasibility of the present Modal Flexibility Variation method (MFV) is verified through some numerical simulation and experimental tests on a steel plate. The results show that the performance of the proposed algorithm is quite encouraging and the maximum differences are less than three percent. 相似文献
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The presently studied numerical model, e.g., composite roughness, is successful for the purpose of seafloor classification employing processed multibeam angular backscatter data from manganese-nodule-bearing locations of the Central Indian Ocean Basin. Hybrid artificial neural network (ANN) architecture, comprised of the self-organizing feature map and learning vector quantization (LVQ), has been implemented as an alternative technique for sea-floor roughness classification, giving comparative results with the aforesaid numerical model for processed multibeam angular backscatter data. However, the composite-roughness model approach is protracted due to the inherent need for processed data including system-gain corrections. In order to establish that tedious processing of raw backscatter values is unessential for efficient classification, hybrid ANN architecture has been attempted here due to its nonparametric approach. In this technical communication, successful employment of LVQ algorithm for unprocessed (raw) multibeam backscatter data indicates true real-time classification application. 相似文献
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An approach based on artificial neural network (ANN) is used to develop predictive relations between hydrodynamic inline force on a vertical cylinder and some effective parameters. The data used to calibrate and validate the ANN models are obtained from an experiment. Multilayer feed-forward neural networks that are trained with the back-propagation algorithm are constructed by use of three design parameters (i.e. wave surface height, horizontal and vertical velocities) as network inputs and the ultimate inline force as the only output. A sensitivity analysis is conducted on the ANN models to investigate the generalization ability (robustness) of the developed models, and predictions from the ANN models are compared to those obtained from Morison equation which is usually used to determine inline force as a computational method. With the existing data, it is found that least square method (LSM) gives less error in determining drag and inertia coefficients of Morison equation. With regard to the predicted results agreeing with calculations achieved from Morison equation that used LSM method, neural network has high efficiency considering its convenience, simplicity and promptitude. The outcome of this study can contribute to reducing the errors in predicting hydrodynamic inline force by use of ANN and to improve the reliability of that in comparison with the more practical state of Morison equation. Therefore, this method can be applied to relevant engineering projects with satisfactory results. 相似文献
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Tsunami run-up height is a significant parameter for dimemsions of coastal structures.In the present study,tsunami run-up heights are estimated by three different Artificial Neural Network (ANN) models,i.e.Feed Forward Back Propagation (FFBP),Radial Basis Functions (RBF) and Generalized Regression Neural Network (GRNN).As the input for the ANN configuration,the wave height (H) values are employed.It is shown that the tsunami run-up height values are closely approximated with all of the applied ANN methods.The ANN estimations are slightly superior to those of the empirical equation.It can he seen that the ANN applications are especially significant in the absence of adequate number of laboratory experiments.The restdts also prove that the available experiment data set can he extended with ANN simulations.This may be helpful to decrease the burden of the experimental studies and to supply results for comparisons. 相似文献
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Iman Malekmohamadi Mohammad Reza Bazargan-Lari Reza Kerachian Mohammad Reza Nikoo Mahsa Fallahnia 《Ocean Engineering》2011,38(2-3):487-497
Wave Height (WH) is one of the most important factors in design and operation of maritime projects. Different methods such as semi-empirical, numerical and soft computing-based approaches have been developed for WH forecasting. The soft computing-based methods have the ability to approximate nonlinear wind–wave and wave–wave interactions without a prior knowledge about them. In the present study, several soft computing-based models, namely Support Vector Machines (SVMs), Bayesian Networks (BNs), Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used for mapping wind data to wave height. The data set used for training and testing the simulation models comprises the WH and wind data gathered by National Data Buoy Center (NDBC) in Lake Superior, USA. Several statistical indices are used to evaluate the efficacy of the aforementioned methods. The results show that the ANN, ANFIS and SVM can provide acceptable predictions for wave heights, while the BNs results are unreliable. 相似文献
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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
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The process of scour around submarine pipelines laid on mobile beds is complicated due to physical processes arising from the triple interaction of waves/currents, beds and pipelines. This paper presents Artificial Neural Network (ANN) models for predicting the scour depth beneath submarine pipelines for different storm conditions. The storm conditions are considered for both regular and irregular wave attacks. The developed models use the Feed Forward Back Propagation (FFBP) Artificial Neural Network (ANN) technique. The training, validation and testing data are selected from appropriate experimental data collected in this study. Various estimation models were developed using both deep water wave parameters and local wave parameters. Alternative ANN models with different inputs and neuron numbers were evaluated by determining the best models using a trial and error approach. The estimation results show good agreement with measurements. 相似文献
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A. V. Polyakov Yu. M. Timofeev Ya. A. Virolainen 《Izvestiya Atmospheric and Oceanic Physics》2014,50(3):330-336
The application of the radiative data inversion technique based on artificial neural networks (ANN) for the meteorological satellite sounding of the atmosphere is described. To increase the efficiency of solving inverse problems, the principal component method is used for the temperature and humidity profiles, as well as for IR radiation spectra, which allows the problem dimensionalities to be reduced substantially. Based on numerical experiments, errors of the temperature and humidity sounding are analyzed from the spectra of outgoing IR radiation (that were measured by the IKFS-2 instrument onboard the Meteor Russian satellite) using the iterative physical-mathematical (IPM) algorithm, multiple linear regression (MLR), and ANN-based methods. Appreciable advantages of the ANN-based method are revealed as compared to the MLR method. Therefore, in temperature sounding, the MLR method has a markedly large error at heights of 1–12 km (a difference of up to 1 K), while the IPM algorithm has almost the same error as the ANN method. The humidity determination error is about 10% when the ANN method is used at heights of 0–12 km. The IPM approach yields approximately the same error in the lower troposphere, but as the height increases the advantages of the ANN method grow. 相似文献
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1 .Introductionanyoffshoreplatformshavebeenbuiltwiththedevelopmentofoceanengineering .However ,mostoffshorearelocatedinseismicregionsandtheplatformsareeasilytobeseriouslydamagedbyearthquakes.Hence ,theanti earthquakedesignhasalwaysbeenamajorpartofresearchonoffshoreplatforms.Theinclusionofvibrationabsorbersintheoffshoreplatformcanbeanattractivemethodofmitigatingseismicresponses .Vibrationabsorberscanbecategorizedintoactiveandpassive .Thetunedmassdamper (TMD)(FujinoandAbe ,1 993) ,eitherpassi… 相似文献
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基于现场实验数据集及人工神经网络技术,论文提出了一种从海中粒子吸收光谱提取浮游植物吸收光谱的方法。这个数据集包含了海中粒子吸收光谱和对应的浮游植物吸收光谱,并被分为三个子集:训练集、印证集和试验集。本研究所利用的人工神经网络系统为多层感知器,训练后的人工神经网络的性能由印证集和试验集来评价。实验结果表明,文中所提出的方法可成功地提取浮游植物的吸收光谱,其提取精度与传统的实验方法相当。 相似文献
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An Artificial Neural Network-Based Response Surface Method for Reliability Analyses of c-? Slopes with Spatially Variable Soil 
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下载免费PDF全文 This paper presents an artificial neural network (ANN)-based response surface method that can be used to predict the failure probability of c-? slopes with spatially variable soil. In this method, the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model; the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties; and finally, an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables. The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability. As a result, the obtained approximate function can be used as an alternative to the specific analysis process in c-? slope reliability analyses. 相似文献
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赤潮预测的人工神经网络方法初步研究 总被引：13，自引：0，他引：13
赤潮是一种由多因素综合作用引发的生态异常现象,具有突发性及非线性等特点。对其进行预测预报一直是海洋科学研究的热点。探讨了应用人工神经网络原理进行赤潮预测的方法,简要介绍了BP和RBF算法的基本原理,用2种算法对不同海域赤潮生物与环境因子之间非线性和不确定性的复杂关系进行学习训练和预测检验,并与传统的统计方法进行了比较。结果表明：人工神经网络方法在模拟和预测方面优于传统的统计回归模型,具有较强的模拟预测能力及实用性,值得进一步探索。 相似文献
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The aim of this paper is to investigate the shape and tension distribution of fishing nets in current. A numerical model is developed, based on lumped mass method to simplify the net. The motion equation is set up for each lumped mass. The Runge–Kutta–Verner fifth-order and sixth-order method is used to solve these simultaneous equations, and then the displacement and tension of each lumped mass are obtained. In order to verify the validity of the numerical method, model tests have been carried out. The results by the numerical simulation agree well with the experimental data. 相似文献
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Burak Aydog˘an Berna Ayat Mehmet N. Öztürk Esin Özkan Çevik Yalçın Yüksel 《Ocean Engineering》2010,37(5-6):443-453
Owing to their complex character, modeling flow patterns of narrow straits has always been a challenge, even with the numerical techniques of today. This study was aimed at predicting vertical current profiles of a given point in a narrow strait, the Strait of Istanbul. On account of the speed and simplicity it offers, and of its remarkable success in solving complex problems, the feed forward back propagation (FFBP) artificial neural network (ANN) technique was chosen for this study. The model was built on 7039 hours of concurrent measurements of current profiles, meteorological conditions, and surface elevations. The model predicted 12 outputs of East and North velocity components at different depths in a given location. Various alternative models with different inputs and neuron numbers were evaluated attaining the best model by trial and error. Predictions from proposed ANN model were in accordance with the observations with average root mean square error of 0.16 m/s. The same input parameters were then used to build models that predicted current velocities 1–12 h into the future. Results of these predictions show good overall agreement with observations and that FFBP ANN can be used as a reliable tool for forecasting current profiles in straits. 相似文献
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When a vessel is damaged, seawater floods into the damaged compartments and subsequently influences the motion of the vessel. Furthermore, the vessel’s behaviour affects the floodwater motion. In this paper, a Navier-Stokes (NS) solver with a free surface capturing technique, i.e., the volume of fluid (VOF) method, was developed to numerically simulate water flooding into a damaged vessel. To verify the developed solver, a 2-D and a 3-D dam break problems were tested. The numerical results coincide well with the experimental results and with the published numerical results. Additionally, it was used to solve the problems of linear and non-linear liquid sloshing in a hexahedral tank. The numerical results are satisfactory in comparison with the experimental results and analytical solutions. Finally, the phenomenon of water flooding into a damaged compartment of a Ro-Ro ferry was simulated numerically. The computed results are in good agreement with the experimental data. 相似文献