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 ground...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.展开更多
提出一种基于NARX(Nonlinear Auto-Regressive Model with Exogenous Inputs)神经网络和谐波探测法的非线性系统传递函数识别方法。该方法可基于实测响应数据,采用NARX神经网络方法对结构响应模型进行训练。在此基础上采用谐波探测法得...提出一种基于NARX(Nonlinear Auto-Regressive Model with Exogenous Inputs)神经网络和谐波探测法的非线性系统传递函数识别方法。该方法可基于实测响应数据,采用NARX神经网络方法对结构响应模型进行训练。在此基础上采用谐波探测法得到系统响应传递函数。选取深海半潜浮式平台及系泊系统为研究对象,计算平台及其系泊系统在不同波浪工况作用下的时域耦合响应,以波高和系泊缆张力时程作为数据集,利用NARX神经网络结合谐波探测法辨识此系泊系统的响应传递函数。采用识别的传递函数预测系泊缆在不同海况下的张力响应,并与数值计算结果进行对比,证明NARX神经网络结合谐波探测法可较好地识别系泊浮体系统的非线性响应传递函数,并能够对系泊系统的张力响应进行准确预测。展开更多
文摘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.
文摘提出一种基于NARX(Nonlinear Auto-Regressive Model with Exogenous Inputs)神经网络和谐波探测法的非线性系统传递函数识别方法。该方法可基于实测响应数据,采用NARX神经网络方法对结构响应模型进行训练。在此基础上采用谐波探测法得到系统响应传递函数。选取深海半潜浮式平台及系泊系统为研究对象,计算平台及其系泊系统在不同波浪工况作用下的时域耦合响应,以波高和系泊缆张力时程作为数据集,利用NARX神经网络结合谐波探测法辨识此系泊系统的响应传递函数。采用识别的传递函数预测系泊缆在不同海况下的张力响应,并与数值计算结果进行对比,证明NARX神经网络结合谐波探测法可较好地识别系泊浮体系统的非线性响应传递函数,并能够对系泊系统的张力响应进行准确预测。
