Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6864157 | Neurocomputing | 2018 | 32 Pages |
Abstract
This paper proposes a new hybrid approach for constructing high-quality prediction intervals (PIs) for landslide displacements. In the first stage, we develop an improved method to optimize bootstrap-based PIs. The improved method uses part of the selected neural networks (NNs) rather than all of the NNs to construct PIs. To guarantee computational efficiency, random vector functional link networks (RVFLNs) are adopted as predictors. In the second stage, to handle the mutational points in landslide displacement prediction, the improved method is integrated with a NN switched method. The effectiveness of the proposed hybrid method has been validated through comprehensive cases using two benchmark data sets and three real-world landslide data sets.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Cheng Lian, Lingzi Zhu, Zhigang Zeng, Yixin Su, Wei Yao, Huiming Tang,