基于长短期记忆网络的黄河小浪底水库高斜心墙堆石坝沉降预测模型及其预测精度分析

针对黄河小浪底水库高斜心墙堆石坝沉降预测精度低,无法准确反映大坝运行性态等问题,基于黄河小浪底水利枢纽主坝沉降监测数据,引入长短期记忆网络(LSTM)方法建立高斜心墙堆石坝沉降预测模型对主坝沉降进行预测,并对不同损失函数下模型优化过程进行分析,最终选定平均绝对误差作为损失函数.预测结果表明,LSTM方法各项指标结果均优...

Settlement Prediction Model of High Inclined Rockfill Dam and Its Prediction Accuracy Analysis Based on LSTM

To solve the problem that the settlement prediction accuracy of high inclined core rockfill dam is low and cannot reflect the exact operation behavior of the dam, LSTM was introduced to establish the settlement prediction model of high inclined core rockfill dam based on the settlement monitoring data of the main dam of Xiaolangdi Hydropower Project of the Yellow River. The optimization process of the model was analyzed for different loss function, and the mean absolute error was chosen as the loss function. The prediction results show that each index of the LSTM is better than stochastic forest algorithm and BP neural network algorithm, which realizes high-precision prediction of operation behavior of high-inclined core rockfill dam, has high practicability. Thus, it provides a guarantee for prediction of reservoir dam settlement and operation and management of the project.