Dam Deformation Monitoring Model and Forecast Based on Hierarchical Diagonal Neural Network

To solve the problem that the forecast accuracy is too weak when adopting former monitoring models to deal with dam monitoring data, a new deformation monitoring model has been put forward based on hierarchical diagonal neural network that can approximate any nonlinear function. The model takes water pressure, temperature and age factors as the input and dam deformation as the output. And then the series-parallel model identifier and dynamic BP learning algorithm play an important role in modeling. The dam deformation data fitting analysis and forecasting research show that the model is not only convergent quickly enough to improve the efficiency of the algorithm, but also has a good effect in fitting with monitoring data. In a word, hierarchical diagonal neural network has great validity and superiority in the dam safety forecast analysis.