Coupling Progressive Deep Learning with the AdaBoost Framework for Landslide Displacement Rate Prediction in the Baihetan Dam Reservoir, China

Disasters caused by landslides pose a considerable threat to people’s lives and property, resulting in substantial losses each year. Landslide displacement rate prediction (LDRP) provides a useful fundamental tool for mitigating landslide disasters. However, more accurately predicting LDRP remains a...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2023-04, Vol.15 (9), p.2296
Main Authors: Ni, Weida, Zhao, Liuyuan, Zhang, Lele, Xing, Ke, Dou, Jie
Format: Article
Language:English
Subjects:
Accuracy
AdaBoost progressive deep learning
Algorithms
Artificial intelligence
China
Dams
Data mining
Deep learning
Disaster management
Emergency communications systems
Environmental aspects
Geology
GRU
Hydroelectric power
landslide displacement rate prediction
Landslides
Landslides & mudslides
Long short-term memory
LSTM
Machine learning
Modelling
Neural networks
Predictions
Rainfall
Recurrent neural networks
Remote sensing
Reservoirs
RNN
Stone
Time series
time series analysis
Topography
Unmanned aerial vehicles
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Summary:Disasters caused by landslides pose a considerable threat to people’s lives and property, resulting in substantial losses each year. Landslide displacement rate prediction (LDRP) provides a useful fundamental tool for mitigating landslide disasters. However, more accurately predicting LDRP remains a challenge in the study of landslides. Lately, ensemble deep learning algorithms have shown promise in delivering a more precise and effective spatial modeling solution. The core aims of this research are to explore and evaluate the prediction capability of three progressive evolutionary deep learning (DL) techniques, i.e., a recurrent neural network (RNN), long short-term memory (LSTM), and a gated recurrent unit (GRU) ensemble AdaBoost algorithm for modeling rainfall-induced and reservoir-induced landslides in the Baihetan reservoir area in China. The outcomes show that the ensemble DL model could predict the Wangjiashan landslide in the Baihetan reservoir area with improved accuracy. The highest accuracy was achieved in the testing set when the window length equaled 30. However, assembling two predictors outperformed the accuracy of assembling three predictors, with the mean absolute error and root mean square error reaching 1.019 and 1.300, respectively. These findings suggest that the combination of strong learners and DL can yield satisfactory prediction results.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15092296