# Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area

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## Abstract

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## 1. Introduction

## 2. Methods and Methods

#### 2.1. Background

**y**= 1/1 + exp(−x). Then, the value of the output layer is calculated using

#### 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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**Figure 1.**Illustration of AE and SAE: ${\mathrm{W}}_{x},\text{}{b}_{x},\text{}{\mathrm{W}}_{y},\text{}{b}_{y}$ represent the weights matrix and bias vector; $x,\text{}t,\text{}y$ represent the input layer, hidden layer, reconstructed layer, respectively.

**Figure 2.**Spatial prediction frameworks of EQIL. Fourteen kinds of related factors are input to the first layer. Softmax is set as a classifier. We adopt sparse optimization to reduce information redundancy.

**Figure 3.**Map of the affected area of the Wenchuan earthquake that is used for model validation. Geology map is collected from China geological survey.

**Figure 4.**Post-earthquake remote sensing image coverage and example of source area extraction of EQIL: (

**a**) remote sensing coverage used in this paper. (

**b**,

**c**) Identification of EQIL source area. (

**d**) Positive samples. (

**e**) Negative sample.

**Figure 5.**Controlling factors deduced from the seismic property, topography, geology, hydrology, and soil datasets.

**Figure 6.**(

**a**) The OA obtained from framework with different hidden units. (

**b**) The OA results from framework with a different number of hidden layers.

**Figure 10.**The feature extraction process of SAE. There are 178 blocks in whole study area. The 34th block is selected as an example of the feature extraction process: (

**a**) fourteen kinds of raw feature were derived from different datasets. (

**b**) Abstract features in first, second, and third layers. (

**c**) Weight matrix of fully connected layer. (

**d**) The probabilities of EQIL and non-EQIL.

**Figure 11.**Spatial prediction of EQIL in bedrock: (a) Location of rock EQIL area (

**b**,

**c**) The UAV images show two typical EQIL in this area. (

**d**) Distribution predicted by proposed method. (

**e**) EQIL obtained from FR. The area is along the Dujiangyan-Wenchuan highway.

**Figure 12.**Controlling factor importance and prediction accuracies of different models. The prediction performance of all methods improved with the increasing number of input-controlling factors; however, the performance of the shallow machine learning model then decreases or remains stable when low-value density data are input, except for the method presented in this work.

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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## Share and Cite

**MDPI and ACS Style**

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

**AMA Style**

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 Style**

Li, 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