地下水位动态变化是一个复杂的水文过程，准确地预测地下水位变化情况在水资源开发利用中有着重要意义。
The change of groundwater level dynamics is a complicate hydrological process. It is important to predict exactly the change of groundwater level in exploiting water resources.
根据其时间序列，建立线性神经网络模型，并将其用于地下水流量的动态预测。
Based on the time series, a model of linear artificial neural network is set and used for dynamic prediction of discharge of groundwater.
据此，可进一步推广到岩溶地下水环境质量的动态管理及污染预测。
On these grounds, it could be spreaded further to dynamic management and pollution calculation of environment quality of karst groundwater.
应用识别与校正后的模型预测不同开采方案下的地下水动态。
The identified and revised model is used to forecast the groundwater regime for different exploitation schemes.
预测结果与实测结果吻合较好，达到了较高精度，该方法对于地下水的动态预报具有一定的实用价值。
Case study indicates that precision of the model is rather high and better than the other models, and has some practical value when being used in the dynamic groundwater level analysis.
用此模型预测了2010年的地下水动态。
提取出的成分具有线性无关的特点，对地下水动态变化有较好的解释能力，利于建模和预测。
The abstracted components, having the property of linearly independence, can explain the groundwater dynamic characteristics better and are more favorable for modeling and forecasting.
地下水物理动态中的井孔水位、水温观测是地震预测预报中重要的前兆观测项目。
Observations of borehole water level and water temperature in the physical dynamics of groundwater are the important observed components of precursores for earthquake prediction.
根据降水量与地下水流量之间的相关关系，建立线性神经网络模型，并且将其用于地下水流量的动态预测。
According to the relationship between precipitation and discharge of groundwater, a model of linear artificial neural network is set and used for dynamic prediction of discharge of groundwater.
研究结果表明本文提出的地下水位动态预测方法是有效的。
The current study on groundwater level in vacuum preloading is analyzed, and some problems are listed.
本文建立了灰色与周期残差叠加模型，并运用模型预测了地下水的变化动态。
Therefore, in this pape we discuss the grey properties of groundwater system, give a superposed model of grey and cyclic error, and forecast the dynamics of the groundwater system by the model.
本文运用反演建模和自记忆性方程相结合的方法，建立了地下水位动态预测模型。
The prediction model of groundwater level dynamics was established using the combinative method of retrieved model and self-memorization equation in this paper.
本文运用反演建模和自记忆性方程相结合的方法，建立了地下水位动态预测模型。
The prediction model of groundwater level dynamics was established using the combinative method of retrieved model and self-memorization equation in this paper.
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