Stability prediction of landslide dams based on SSA-Adam-BP neural network model

Abstract

Most of the existing landslide dam stability prediction models are linear models, which cannot fully consider the complex nonlinear relationship between landslide dam stability and its morphological characteristics and hydrodynamic conditions.In view of this, a new SSA-Adam-BP model for predicting the stability of landslide dams is proposed by combining the back propagation neural network model and the salp optimization algorithm.The grid search method is used to select the best combination of hyperparameters that can determine the structure of the model.Then, the models with different optimization algorithms are evaluated by cross-validation and ROC curve drawing.The practical application of the model is explained and verified by using the global data of 153 landslide dams in the open source database.Compared with the traditional linear model, the combination of the SSA and Adam optimization algorithm improves the global search ability of the BP model, and its average cross-verification accuracy reaches 91.73%.It not only has a lower misjudgment rate but can also use fewer parameters to quickly and accurately predict the stability of landslide dams.The SSA-Adam-BP model can accurately predict the stability of typical projects in recent years, with certain practicality and system platform promotion application value.

Keywords

• landslide dam stability
• machine learning
• stability prediction
• bp neural network
• ssa optimization algorithm