Name
Abstract | ||
|---|---|---|
Landslide deformation prediction has significant practical value that can provide guidance for preventing the disaster and guarantee the safety of people's life and property. In this paper, a method based on recurrent neural network (RNN) for landslide prediction is presented. Genetic algorithm is used to optimize the initial weights and biases of the network. The results show that the prediction accuracy of RNN model is much higher than the feedforward neural network model for Baishuihehe landslide. Therefore, the RNN model is an effective and feasible method to further improve accuracy for landslide displacement prediction. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s11063-013-9318-5 | Neural Processing Letters |
Keywords | Field | DocType |
Landslide,Deformation prediction,RNN,Elman network,Genetic algorithm | Feedforward neural network,Recurrent neural network,Landslide,Artificial intelligence,Deformation (mechanics),Machine learning,Mathematics,Genetic algorithm | Journal |
Volume | Issue | ISSN |
41 | 2 | 1370-4621 |
Citations | PageRank | References |
4 | 0.49 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huangqiong Chen | 1 | 28 | 2.40 |
| Zhigang Zeng | 2 | 3962 | 234.23 |
| Huiming Tang | 3 | 57 | 13.06 |