基于布谷鸟搜索算法优化LSTM的大坝变形预测

对大坝变形进行合理分析和准确预测是确保大坝安全运行的重要手段.大坝变形监测数据具有趋势性、周期性、随机性和非线性等特性，现有的机器学习模型大都基于大坝变形监测数据的非线性特点进行构建，而忽略了监测数据还具有趋势性和周期性的线性特征.提出了一种大坝变形预测模型，通过采用布谷鸟搜索算法(CS)对长短期记忆人工神经网络(LSTM)进行优化，再基于物联网传感器的实时监测数据，使用局部加权回归的周期趋势分解方法(STL)将数据分解成趋势分量、周期分量和余项分量，采用优化后的LSTM模型对趋势分量和余项分量分别进行预测，并通过简单周期估计方法进行计算，将3个分量的预测结果求和后得到最终变形预测结果.试验选取浙江利山水库开展变形预测研究，结果表明：STL-CS-LSTM模型的水平和沉降变形预测精度都高于其他模型，水平位移预测精度由高到低依次为LSTM模型、支持向量回归模型SVR和人工神经网络模型ANN,沉降预测精度由高到低依次为ANN模型、LSTM模型、SVR模型.

Dam deformation prediction based on optimization of LSTM by using cuckoo search algorithm

Reasonable analysis and accurate prediction of dam deformation is an important means to ensure the safe operation of the dam′s safety management. The dam deformation prediction data has the characteristics of tendency, periodicity, randomness and nonlinearity. Most of the existing machine learning models are based on the nonlinear characteristics of dam deformation prediction data, while ignoring the linear characteristics of trend and periodicity of prediction data. A dam deformation prediction model was proposed by using the optimized cuckoo search algorithm(CS), the long-term and short-term memory artificial neural network(LSTM). Based on the real-time monitoring data of IoT sensors, an STL-CS-LSTM combination model was proposed. The model decomposed the dam deformation time series data into trend component, periodic component, and residual component by using the seasonal trend decomposition procedure based on the loess(STL)method of locally weighted regression. Then the optimized LSTM model was used to predict the trend component and the remainder component respectively. The simple period estimation method was used for prediction calculation. Finally, the final deformation prediction result was obtained by adding the prediction results of the three components. Lishan reservoir in Zhejiang Province was selected to carry out a deformation prediction experiment using the data of horizontal displacement and settlement automatically obtained by IoT. The results show that the STL-CS-LSTM model has the best prediction performance both in horizontal displacement and settlement deformation. The horizontal displacement prediction accuracy of other models from high to low are LSTM model, support vector regression model SVR and artificial neural network model ANN. The settlement prediction accuracy of other models is ANN model, LSTM model and SVR model.