地震是地壳运动的一种表现,地震的发生与地下岩石的应力状态密切相关。井位水位的变化可能反映地下岩石的应力状态的变化,通过神经网络预测井位水位,可以提前获得地下水位的变化情况,为地震灾害的评估和应对提供依据,减少灾害的损失。...地震是地壳运动的一种表现,地震的发生与地下岩石的应力状态密切相关。井位水位的变化可能反映地下岩石的应力状态的变化,通过神经网络预测井位水位,可以提前获得地下水位的变化情况,为地震灾害的评估和应对提供依据,减少灾害的损失。为了解湖北雷庄地下水位动态,进而分析地震前兆动态,本文设计了一个基于BP神经网络的地下水位预测模型。采用SWY-II数字式水位仪对雷庄地下水位数据进行采集。根据采集的2019~2020年水位数据,利用BP神经网络对地下水位变化进行预测,以一年的采集数据进行训练和测试,采用1个输入节点、3个隐含节点、1个输出节点设计了BP神经网络结构。为了进一步验证本预测模型,本文对2020年01月01日~12月31日地下水位进行了预测。实验表明:该模型能有效实现地下水位的预测,为地震前兆工作提供可靠数据。Earthquakes are a manifestation of crustal movement, and their occurrence is closely related to the stress state of underground rocks. The changes in water level at the well site may reflect the changes in the stress state of underground rocks. By predicting the water level at the well site through neural networks, the changes in groundwater level can be obtained in advance, providing a basis for evaluating and responding to earthquake disasters, and reducing disaster losses. In order to understand the groundwater level dynamics in Leizhuang, Hubei and analyze the earthquake precursor dynamics, we design a groundwater level prediction model based on BP neural network. The SWY-II digital water level meter is used to collect groundwater level data in Leizhuang. Based on the collected water level data from 2019 to 2020, a BP neural network was used to predict changes in groundwater level. One year of collected data was used for training and testing. A BP neural network structure was designed using one input node, three hidden nodes, and one output node. In order t展开更多
文摘地震是地壳运动的一种表现,地震的发生与地下岩石的应力状态密切相关。井位水位的变化可能反映地下岩石的应力状态的变化,通过神经网络预测井位水位,可以提前获得地下水位的变化情况,为地震灾害的评估和应对提供依据,减少灾害的损失。为了解湖北雷庄地下水位动态,进而分析地震前兆动态,本文设计了一个基于BP神经网络的地下水位预测模型。采用SWY-II数字式水位仪对雷庄地下水位数据进行采集。根据采集的2019~2020年水位数据,利用BP神经网络对地下水位变化进行预测,以一年的采集数据进行训练和测试,采用1个输入节点、3个隐含节点、1个输出节点设计了BP神经网络结构。为了进一步验证本预测模型,本文对2020年01月01日~12月31日地下水位进行了预测。实验表明:该模型能有效实现地下水位的预测,为地震前兆工作提供可靠数据。Earthquakes are a manifestation of crustal movement, and their occurrence is closely related to the stress state of underground rocks. The changes in water level at the well site may reflect the changes in the stress state of underground rocks. By predicting the water level at the well site through neural networks, the changes in groundwater level can be obtained in advance, providing a basis for evaluating and responding to earthquake disasters, and reducing disaster losses. In order to understand the groundwater level dynamics in Leizhuang, Hubei and analyze the earthquake precursor dynamics, we design a groundwater level prediction model based on BP neural network. The SWY-II digital water level meter is used to collect groundwater level data in Leizhuang. Based on the collected water level data from 2019 to 2020, a BP neural network was used to predict changes in groundwater level. One year of collected data was used for training and testing. A BP neural network structure was designed using one input node, three hidden nodes, and one output node. In order t
