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1.
The time series of the dynamic response of a slender marine structure was predicted in approximate sense using a truncated quadratic Volterra series. The wave-structure interaction system was identified using the NARX (Nonlinear Autoregressive with Exogenous Input) technique, and the network parameters were determined through supervised training using prepared datasets. The dataset used for network training was obtained by nonlinear finite element analysis of the slender marine structure under random ocean waves of white noise. The nonlinearities involved in the analysis were both large deformation of the structure under consideration and the quadratic term of the relative velocity between the water particle and structure in the Morison formula. The linear and quadratic frequency response functions of the given system were extracted using the multi-tone harmonic probing method and the time series of the response of the structure was predicted using the quadratic Volterra series. To check the applicability of the method, the response of a slender marine structure under a realistic ocean wave environment with a given significant wave height and modal period was predicted and compared with the nonlinear time domain simulation results. The predicted time series of the response of structure with quadratic Volterra series successfully captured the slowly varying response with reasonably good accuracy. This method can be used to predict the response of the slender offshore structure exposed to a Morison type load without relying on the computationally expensive time domain analysis, especially for screening purposes.  相似文献   
2.
Cross-shore migratory behavior of nearshore sandbars is commonly studied with nearshore bathymetric-evolution models that represent underlying processes of hydrodynamics and sediment transport. These models, however, struggle to reproduce natural cross-shore sandbar behavior on timescales of a few days to weeks and have uncertain skill on longer scales of months to years. One particular concern for the use of models on prediction timescales that far exceed the timescale of the modeled processes is the exponential accumulation of errors in the nonlinear model equations. The relation between cross-shore sandbar migration, sandbar location and wave height has previously been demonstrated to be weakly nonlinear on timescales of several days, but it is unknown how this nonlinearity affects the predictability of long-term (months to years) cross-shore sandbar behavior. Here we study the role of nonlinearity in the predictability of sandbar behavior on timescales of a few days to several months with data-driven neural network models. Our analyses are based on over 5600 daily-observed cross-shore sandbar locations and daily-averaged wave forcings from the Gold Coast, Australia, and Hasaki, Japan. We find that neural network models are able to hindcast many aspects of cross-shore sandbar behavior, such as rapid offshore migration during storms, slower onshore return during quiet periods, seasonal cycles and annual to interannual offshore-directed trends. Although the relation between sandbar migration, sandbar location and wave height is nonlinear, sandbar behavior can be hindcasted accurately over the entire lifespan of the sandbars at the Gold Coast. Contrastingly, it is difficult to hindcast the long-term offshore-directed trends in sandbar behavior at Hasaki because of exponential accumulation of errors over time. Our results further reveal that during periods with low-wave conditions it becomes increasingly difficult to predict sandbar locations, while during high waves predictions become increasingly accurate.  相似文献   
3.
为了得到精确度较高的降雨量预测值及其叠加预测精度,利用小波神经网络和NARX动态神经网络对降雨趋势和降雨量进行预测,并分析降雨量叠加预测值的误差。研究表明,小波神经网络分析的月降雨量多个变化周期以及总的变化趋势较为准确;NARX动态神经网络预测模型测试误差为0.21%,回归效果图的相关系数R为0.99993,回判和检验误差分别只有0.22%和0.40%;降雨量叠加预测和检验误差较小,均未超过2%,能够满足降雨量不断叠加预测的要求。该方法能为边坡动态稳定性预测提供精确度较高的降雨量预测值。  相似文献   
4.
ABSTRACT

This paper presents a neural network model capable of catchment-wide simultaneous prediction of river stages at multiple gauging stations. Thirteen meteorological parameters are considered in the input, which includes rainfall, temperature, mean relative humidity and evaporation. The NARX model is trained with a representative set of hourly data, with optimal time delay for both the input and output. The network trained using 120-day data is able to produce simulations that are in excellent agreement with field observations. We show that for application with one-step-ahead predictions, the loss in network performance is marginal. Inclusion of additional tidal observations does not improve predictions, suggesting that the river stage stations under consideration are not sensitive to tidal backwater effects despite the claim commonly made.
EDITOR D. Koutsoyiannis ASSOCIATE EDITOR F. Pappenberger  相似文献   
5.
Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of groundwater resources. In this study, a new nonlinear autoregressive with exogenous inputs(NARX) network has been applied to simulate monthly groundwater levels in a well of Sylhet Sadar at a local scale. The Levenberg-Marquardt(LM) and Bayesian Regularization(BR) algorithms were used to train the NARX network, and the results were compared to determine the best architecture for predicting monthly groundwater levels over time. The comparison between LM and BR showed that NARX-BR has advantages over predicting monthly levels based on the Mean Squared Error(MSE), coefficient of determination(R~2), and Nash-Sutcliffe coefficient of efficiency(NSE). The results show that BR is the most accurate method for predicting groundwater levels with an error of ± 0.35 m. This method is applied to the management of irrigation water source, which provides important information for the prediction of local groundwater fluctuation at local level during a short period.  相似文献   
6.
The tremendous increase in offshore operational activities demands improved wave forecasting techniques. With the knowledge of accurate wave conditions, it is possible to carry out the marine activities such as offshore drilling, naval operations, merchant vessel routing, nearshore construction, etc. more efficiently and safely. This paper describes an artificial neural network, namely recurrent neural network with rprop update algorithm and is applied for wave forecasting. Measured ocean waves off Marmugao, west coast of India are used for this study. Here, the recurrent neural network of 3, 6 and 12 hourly wave forecasting yields the correlation coefficients of 0.95, 0.90 and 0.87, respectively. This shows that the wave forecasting using recurrent neural network yields better results than the previous neural network application.  相似文献   
7.
To minimize the computation burdens of long-term analysis, the mooring line top tensions under various short-term sea states were predicted using the pre-calculated nonlinear time domain analysis results of some selected sea states. A nonlinear autoregressive with an exogenous input (NARX) technique was introduced with a finite nonlinear memory length to predict the top-tension of a mooring line of the box-type floating production storage and offloading (FPSO). The NARX was designed in such a way that it takes the floater’s motions as its input and produces the mooring line top tension as an output. After training the NARX using the pre-calculated time series of the motions and tensions of some selected sea states, the tension time histories of different sea sates were predicted and compared with the direct time domain analysis results. In addition, to explore the nonlinearity of the system and its contribution to the response, the transfer functions of different orders were extracted after expanding the NARX equation using a Taylor series expansion. The nonlinear contributions coming from both the 3rd and 5th order were significant.  相似文献   
8.
Analysis and forecasting of water temperature are important for water ecological management. The objective of this study is to compare models for water temperature during the summer season for an impounded river. In a case study, we consider hydro-climatic and water temperature data for the Fourchue River (St-Alexandre-de-Kamouraska, Quebec, Canada) between 2011 and 2014. Three different models are applied, which are broadly characterized as deterministic (CEQUEAU), stochastic (Auto-regressive Moving Average with eXogenous variables or ARMAX) and nonlinear (Nonlinear Autoregressive with eXogenous variables or NARX). The efficiency of each model is analysed and compared. The results show that the ARMAX is the best performing water temperature model for the Fourchue River and the CEQUEAU model also simulates water temperature adequately without the overfitting issues that seem to plague the autoregressive models.
EDITOR M.C. Acreman

ASSOCIATE EDITOR R. Hirsch  相似文献   
9.
基于自回归神经网络的时间序列叶面积指数估算   总被引:2,自引:0,他引:2  
叶面积指数LAI是众多气象、环境、农业等模型的关键输入参数。尽管具有多个传感器的全球LAI产品已经相继发布,但是由于受反演方法的局限性以及反射率产品质量的影响,这些由单一传感器数据得到的LAI产品在时间上表现出一定的不连续性,这与自然生长植被的LAI变化规律不能一致。而神经网络在对复杂的、非线性数据的模式识别能力方面具有出色的表现。如在3层神经网络中,只要对隐层采用非线性递增映射函数,输出层采用线性映射函数,就可以用于对任意连续函数进行逼近。对于具有相同植被覆盖类型的同一地点多年的LAI数据,在无自然灾害和人为破坏的前提下,可以构成一个非线性的、连续的时间序列。通过融合MODIS和VEGETATION两种传感器产品,在利用相同植被类型的LAI时间序列来建立自回归神经网络,即NARX神经网络的同时,引入红、近红外和短波红外3个波段上时间序列的反射率以及相应的太阳天顶角、观测天顶角和相对方位角作为NARX神经网络的外部输入变量,并最终达到估算时间序列LAI的目的。验证结果表明,NARX神经网络非常适用于时间序列的LAI估算,并且其预测的LAI比原始的MODIS LAI在时间序列上表现的更连续和平滑。因此,该方法在...  相似文献   
10.
《国际泥沙研究》2022,37(6):766-779
Sediment forecasting at a dam site is important for the operation and management of water and sediment in a reservoir. However, the forecast results generally have some uncertainties, which may hinder the operation of the dam. In this study, a real-time sediment concentration probabilistic forecasting model is proposed based on a dynamic network model. Under this framework, the Elman neural network (ENN) and nonlinear auto-regressive with exogenous inputs (NARX) neural network models were established for sediment concentration forecasting with different lead times. A hybrid algorithm, which combined the Levenberg–Marquardt algorithm and real-time recurrent learning, was used to train the model. Using the aforementioned method, the sediment concentration was forecast for at the Sanmenxia Dam, China, and, subsequently, the forecast results were evaluated. Among the selected lead time, the results at 5 h exhibited the highest accuracy and practical significance. Compared with the ENN model, the sediment concentration peak error using the NARX neural network was reduced by 4.5%, and the sediment yield error was reduced by 0.043%. Therefore, the NARX neural network was selected as the deterministic sediment forecasting model. Additionally, the probability density function of the sediment concentration was derived based on the heterogeneity of the error distribution, and the sediment concentration interval, with different confidence levels, expected values, and median values, was forecast. The Nash–Sutcliffe coefficient of efficiency for the sediment concentration, as forecasted based on the median value, was the highest (0.04 higher than that using a deterministic model), whereas the error of the sediment concentration peak and sediment yield remained unaltered. These results indicated the accuracy and superiority of the proposed real-time sediment probabilistic forecasting hybrid model.  相似文献   
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