# Combining Numerical Simulation and Deep Learning for Landslide Displacement Prediction: An Attempt to Expand the Deep Learning Dataset

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Jiuxianping Landslide

#### 2.1. Regional Background and Geological Structure

^{2}, and a volume of about 5.7 × 107 m

^{3}, and it was a large bedding rock landslide with the front edge wading. There are three monitoring sections in the east, middle and west of the landslide. This paper selected the monitoring data and geological data of the II-II′ section in the middle of the landslide for study.

#### 2.2. Monitoring Results

## 3. Coupled Numerical Simulation Schemes

#### 3.1. Numerical Model

#### 3.2. Simulation Schemes and Parameters

#### 3.3. Simulation Results

## 4. Displacement Time Series Similarity

#### 4.1. Numerical Similarity

#### 4.2. Orientation Similarity

#### 4.3. Shape Similarity

## 5. Displacement Prediction Deep Learning Model

#### 5.1. Deep Learning Model Construction

#### 5.1.1. CNN Module

#### 5.1.2. BiGRU Module

#### 5.1.3. AM Module

#### 5.2. Model Training and Prediction

## 6. Discussion

## 7. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 4.**Images of landslide surface deformation. (

**a**) Front of the landslide. (

**b**) Mountain road cracks and uplifts. (

**c**) Cracks in the steps. (

**d**) Deformation of the buildings.

**Figure 5.**Precipitation, reservoir water level and accumulated displacement at section II-II′ of the Jiuxianping landslide.

**Figure 10.**DTW similarity test between simulated and monitored displacements. (

**a**) Simulated displacement, monitored displacement and its fitted cubic function curve. (

**b**) Alignment paths between simulated and monitored displacement series. (

**c**) Best dynamical time warping path.

**Figure 12.**Results of training set and testing set. (

**a**) Based on monitored displacement. (

**b**) Based on simulated displacement.

**Figure 13.**Distribution of absolute errors of prediction. (

**a**) Statistical frequency distribution of prediction errors. (

**b**) Box plot of prediction errors.

Materials | Unit Weight(kN/m^{3}) | Elastic Modulus(MPa) | Cohesion(kPa) | Poisson Ratio | Friction Angle(°) | Permeability Coefficient (m/d) |

Sliding mass | 23.8 | 306 | 800 | 0.25 | 33.9 | 0.12 |

Sliding zone | 18.4 | 12.5 | 20.2 | 0.28 | 18 | 0.468 |

Bedrock | 25.2 | 1120 | - | 0.22 | - | 0.005 |

**Table 2.**Root mean square error (RMSE), mean absolute error (MAE) and DTW distance between the curves.

Metric | RMSE (mm) | MAE (mm) | DTW Distance (mm) |
---|---|---|---|

Monitored-Simulated | 17.58 | 14.39 | 633.47 |

Monitored-Fitted | 6.91 | 5.06 | 642.84 |

Data Set | Date Span | Data Length | |
---|---|---|---|

Training set | Monitored data | June 2016–June 2020 | 1470 |

Simulated data | June 2016–June 2020 | 49 | |

Testing set | Monitored data | July 2020–June 2021 | 12 |

Hyperparameter | Dropout | Batch Size | Filter Length | GRU Units |
---|---|---|---|---|

Training with monitored data | 0 | 16 | 4 | 32 |

Training with simulated data | 0.1 | 64 | 4 | 32 |

Metric | MAE (mm) | RMSE (mm) | MAPE (%) |
---|---|---|---|

Prediction with monitored training set | 3.99 | 4.17 | 2.68 |

Prediction with simulated training set | 1.23 | 1.50 | 0.85 |

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**MDPI and ACS Style**

Xu, W.; Xu, H.; Chen, J.; Kang, Y.; Pu, Y.; Ye, Y.; Tong, J.
Combining Numerical Simulation and Deep Learning for Landslide Displacement Prediction: An Attempt to Expand the Deep Learning Dataset. *Sustainability* **2022**, *14*, 6908.
https://doi.org/10.3390/su14116908

**AMA Style**

Xu W, Xu H, Chen J, Kang Y, Pu Y, Ye Y, Tong J.
Combining Numerical Simulation and Deep Learning for Landslide Displacement Prediction: An Attempt to Expand the Deep Learning Dataset. *Sustainability*. 2022; 14(11):6908.
https://doi.org/10.3390/su14116908

**Chicago/Turabian Style**

Xu, Wenhan, Hong Xu, Jie Chen, Yanfei Kang, Yuanyuan Pu, Yabo Ye, and Jue Tong.
2022. "Combining Numerical Simulation and Deep Learning for Landslide Displacement Prediction: An Attempt to Expand the Deep Learning Dataset" *Sustainability* 14, no. 11: 6908.
https://doi.org/10.3390/su14116908