台风暴雨型滑坡地下水位动态特征及预测

Analysis and prediction of the groundwater dynamics of landslide induced by typhoon rainstorm

台风暴雨常引起强烈的地下水位波动,间接影响了地质体的稳定性,是东南沿海地区滑坡发生的主要诱因,准确预测降雨作用下的地下水位对该类滑坡的防治及预警预报具有重要意义。RBF神经网络通过对样本数据进行人工智能分析,可无限逼近任意非线性函数值,适用于滑坡地下水位动态预测。该文基于浙江中林滑坡位移、降雨、地下水位等长期监测数据,分析台风暴雨型滑坡渗流与形变特征,探讨降雨与地下水位之间响应关系。通过MATLAB软件平台确定径向基的宽度,并建立地下水位动态预测模型。通过对地下水位实测值与预测值进行对比分析,得出实测值与预测值的偏差最小值为0.01 m,最大值为3.13 m,平均值为0.46 m;同级别降雨量的样本数量越多,预测结果越精确。研究表明,RBF神经网络在地下水位预测方面具有一定的实际应用意义。

Strong fluctuation in groundwater level are often caused by typhoon rainstorm,which indirectly affects the stability of geological bodies,and was the primary cause of landslides in southeastern coast area.Therefore,an accurate prediction of groundwater level under rainfall was of critical significance to prevent and early warn of this type of landslides.RBF neural network,that could infinitely approximate any nonlinear function value with AI analysis of sample data,was suitable for dynamically predicting landslides'groundwater level.Based on the long-term monitoring data of Zhonglin landslide,such as displacement,rainfall and groundwater level,this paper analyzed the seepage and deformation characteristics of typhoon rainstorm-type landslide,discussed the corresponding relation between rainfall and groundwater level.The width of radial base was determined by MATLAB software training,and a dynamic prediction model of groundwater level was established thereby.Then,through the comparison between measured and predicted values of the groundwater level,it was concluded that a minimum deviation value between measured and predicted was 0.01 m,a maximum value 3.13 m,and a mean value 0.46 m.In addition,the more the number of samples at the same rainfall level was,the more accurate the predicted result would be.The research showed that RBF neural network was of practical significance in groundwater level prediction.