Reliability-enhanced surrogate-assisted particle swarm optimization for feature selection and hyperparameter optimization in landslide displacement prediction

Abstract

Abstract Landslides are dangerous disasters that are affected by many factors. Neural networks can be used to fit complex observations and predict landslide displacement. However, hyperparameters have a great impact on neural networks, and each evaluation of a hyperparameter requires the construction of a corresponding model and the evaluation of the accuracy of the hyperparameter on the test set. Thus, the evaluation of hyperparameters requires a large amount of time. In addition, not all features are positive factors for predicting landslide displacement, so it is necessary to remove useless and redundant features through feature selection. Although the accuracy of wrapper-based feature selection is higher, it also requires considerable evaluation time. Therefore, in this paper, reliability-enhanced surrogate-assisted particle swarm optimization (RESAPSO), which uses the surrogate model to reduce the number of evaluations and combines PSO with the powerful global optimization ability to simultaneously search the hyperparameters in the long short-term memory (LSTM) neural network and the feature set for predicting landslide displacement is proposed. Specifically, multiple surrogate models are utilized simultaneously, and a Bayesian evaluation strategy is designed to integrate the predictive fitness of multiple surrogate models. To mitigate the influence of an imprecise surrogate model, an intuitional fuzzy set is used to represent individual information. To balance the exploration and development of the algorithm, intuition-fuzzy multiattribute decision-making is used to select the best and most uncertain individuals from the population for updating the surrogate model. The experiments were carried out in CEC2015 and CEC2017. In the experiment, RESAPSO is compared with several well-known and recently proposed SAEAs and verified for its effectiveness and advancement in terms of accuracy, convergence speed, and stability, with the Friedman test ranking first. For the landslide displacement prediction problem, the RESAPSO-LSTM model is established, which effectively solves the feature selection and LSTM hyperparameter optimization and uses less evaluation time while improving the prediction accuracy. The experimental results show that the optimization time of RESAPSO is about one-fifth that of PSO. In the prediction of landslide displacement in the step-like stage, RESAPSO-LSTM has higher prediction accuracy than the contrast model, which can provide a more effective prediction method for the risk warning of a landslide in the severe deformation stage.

Keywords

• Computationally expensive problems
• Surrogate-assisted evolutionary algorithm
• Particle swarm optimization
• Hyperparameter optimization
• Feature selection