Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area
Abstract
:1. Introduction
2. Methods and Methods
2.1. Background
2.2. Prediction Framework
2.3. Model Evaluation
3. EQIL Inventory
3.1. Study Area
3.2. Training and Testing Samples
3.3. Training and Testing Samples
4. Experiment and Results
4.1. Framework Setting
4.2. Visualizing Result and Performance Assessment
5. Discussion
5.1. High-Level Feature Representation
5.2. Performance of Rock Landslide Prediction
5.3. Influence on Factor Importance
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Actually Positive (1) | Actually Negative (0) | |
---|---|---|
Predicted Positive (1) | True Positives (TP) | False Positives (FP) |
Predicted Negative (0) | False Negatives (FN) | True Negatives (TN) |
Category Name | No. of Pixels | No. of Training Samples | No. of Testing Samples |
---|---|---|---|
EQIL | 819,389 | 163,877 | 655,512 |
Non-EQIL | 819,389 | 163,877 | 655,512 |
Total | 1,638,778 | 327,754 | 1,211,024 |
Category | Control Factors | Data Type | Data Source |
---|---|---|---|
Seismic property | EI—Earthquake intensity | Polygon | China Earthquake Administration (CEA) |
ED—Epicenter directivity | Point | ||
SRD—Surface rupture directivity | Polyline | ||
AF—Aftershocks | Point | ||
Topography | DEM (12.5 m resolution) | Raster | Alaska Satellite Facility, USA |
SLO—Slope gradients | Raster | ||
SLOA—Slope aspect | Raster | ||
TPI—Topographic position index [47] | Raster | ||
SC—Slope curvature | Raster | ||
RER—Relative relief | Raster | ||
Geology | LITH—Lithology | Polygon | China Geological Survey |
FD—Fault direction | Polyline | ||
Hydrology | DR—Distance to rivers | Polyline | Department of Forestry, Sichuan Province |
Soil | ST—Soil type | Polygon | Department Natural Resources, Sichuan Province |
Learning Rate | 0.0001 | 0.001 | 0.01 | 0.1 | 0.8 |
---|---|---|---|---|---|
OA (%) | 80.35 ± 0.40 | 83.03 ± 0.05 | 83.84 ± 0.10 | 85.49 ± 0.16 | 86.72 ± 0.23 |
Precision (%) | 79.85 ± 0.45 | 81.91 ± 0.07 | 82.45 ± 0.13 | 84.14 ± 0.88 | 85.37 ± 0.96 |
Recall (%) | 81.24 ± 0.65 | 84.83 ± 0.001 | 86.02 ± 0.11 | 87.53 ± 1.25 | 88.68 ± 1.9 |
Measurements | Logistic Regression | Support Vector Machine | Random Forest | Proposed Method |
---|---|---|---|---|
OA (%) | 80.75 ± 0.23 | 82.22 ± 0.15 | 84.16 ± 0.22 | 91.88 ± 0.18 |
Precision of EQIL (%) | 79.10 ± 0.34 | 80.70 ± 0.23 | 81.93 ± 0.17 | 87.56 ± 0.21 |
Recall of EQIL (%) | 80.33 ± 0.27 | 82.07 ± 0. 12 | 84.40 ± 0. 15 | 91.40 ± 0. 20 |
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Li, Y.; Cui, P.; Ye, C.; Junior, J.M.; Zhang, Z.; Guo, J.; Li, J. Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area. Remote Sens. 2021, 13, 3436. https://doi.org/10.3390/rs13173436
Li Y, Cui P, Ye C, Junior JM, Zhang Z, Guo J, Li J. Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area. Remote Sensing. 2021; 13(17):3436. https://doi.org/10.3390/rs13173436
Chicago/Turabian StyleLi, Yao, Peng Cui, Chengming Ye, José Marcato Junior, Zhengtao Zhang, Jian Guo, and Jonathan Li. 2021. "Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area" Remote Sensing 13, no. 17: 3436. https://doi.org/10.3390/rs13173436