# A Landslide Displacement Prediction Model Based on the ICEEMDAN Method and the TCN–BiLSTM Combined Neural Network

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methods

#### 2.1. Empirical Mode Decomposition

#### 2.2. Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

#### 2.3. Temporal Convolutional Network

#### 2.3.1. Causal Dilated Convolutional

#### 2.3.2. Residual Block

#### 2.4. BiLSTM

#### 2.5. TCN–BiLSTM

#### 2.6. Evaluation Index

^{2}), and mean absolute percentage error (MAPE), to assess the model’s performance in terms of accuracy and applicability. The following is the calculation formula for measurement indicators:

^{2}, the evaluation is conducted, and the calculation results of all three belong to the range of 0 to 1.

## 3. Overview of Study Area

#### 3.1. General Description

^{2}. Silty clay containing broken stones makes up most of the sliding soil, and the gravel is mostly composed of sandstone blocks. The landslide volume is around 38.4 × 104 m

^{3}, and the landslide mass has an average thickness of roughly 10 m. The sliding zone is a soft plastic silty clay with a small amount of breccia during the period, which has poor water permeability and can form a relatively impermeable layer. The sliding bed is an underlying bedrock, mainly composed of strongly to moderately weathered carbonaceous sandy mudstone and sandstone.

#### 3.2. Macroscopic Deformation Characteristics

#### 3.3. Analysis of Monitoring Data and Triggering Factors

## 4. Results

#### 4.1. Data Decomposition

#### 4.2. Determining Influencing Factors

#### 4.3. Displacement Prediction

#### 4.3.1. Trend Item Displacement Prediction

^{2}of 0.999, indicating an excellent fitting effect.

^{2}was 0.999. The calculation results of the evaluation indicators show that using the cubic polynomial fitting method for trend displacement prediction can achieve good prediction results.

#### 4.3.2. Prediction of Fluctuation Term Displacement

- Due to the limited landslide monitoring data provided in the engineering project, the generalization performance of the trained LSTM model was poor, resulting in an R
^{2}of 0.915 and unsatisfactory prediction performance. - Due to their unique bidirectional processing structure, BiLSTM neural networks typically provide richer feature representations, which means they can better capture patterns and relationships in input sequences. From the overall error distribution, the prediction error of BiLSTM was slightly lower than LSTM’s. The final calculation result of LSTM neural network R
^{2}was 0.945, and the prediction effect was better than that of LSTM. - The TCN evolved from convolutional neural networks requires fewer parameters than LSTM, making it easier to train and adjust. Therefore, TCN neural networks can achieve more accurate displacement prediction even with limited training data support. The final TCN neural network R
^{2}calculation result was 0.945, and the prediction effect was better than that of LSTM.

#### 4.3.3. Total Displacement Prediction

^{2}was 0.999, indicating a good prediction effect.

## 5. Conclusions

- (1)
- The ICEEMDAN algorithm has strong adaptability to decomposing landslide displacement sequences. By selecting a reasonable signal-to-noise ratio decomposition, the cumulative displacement of landslides can be effectively decomposed into relatively stable, high-frequency fluctuation terms and low-frequency residual terms, and the resulting displacement components have practical physical significance.
- (2)
- In the selection of characteristic data for predicting landslide displacement fluctuation terms, precipitation, the groundwater level, and the historical displacement of landslides are highly correlated with the displacement components of landslide fluctuation terms. This article used the GRG–MCI combination screening method to process the processed parameter data, and the influencing factors identified were highly correlated with the displacement component of the landslide fluctuation term.
- (3)
- For landslide trend displacement prediction, using the polynomial fitting method can achieve good prediction results, with a predicted value of R
^{2}of 0.999, which indicates high prediction accuracy and can accurately reflect the trend changes of landslide displacement. In predicting the displacement of landslide fluctuation terms, the TCN–BiLSTM combined structural neural network model can accurately capture the fluctuation changes of landslide displacement, with a predicted value of R^{2}of 0.997, which performs better than the conventional LSTM, TCN, BiLSTM, and TCN–LSTM models. - (4)
- This article used the ICEEMDAN–TCN–BiLSTM model to predict the displacement of the D1 monitoring point of the Wanjiawan landslide. The various evaluation indicators of the predicted results prove that the model has high applicability for landslide displacement prediction. Based on this, it was inferred that this method can be effectively used to predict displacement at other landslide locations. However, its applicability in predicting the displacement of other types of landslides still needs further verification.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Landslide displacement prediction flow chart: (

**a**) data processing, (

**b**) displacement prediction, and (

**c**) model validation.

**Figure 6.**Wanjiawan landslide: (

**a**) landslide boundary and layout of monitoring point and (

**b**) geological profile along sections II–II′.

**Figure 7.**Photos of landslide fissure development: (

**a**) cracks at the rear edge of the landslide, (

**b**) cracks in the flat area of the engineering site, (

**c**) cracks in the middle of the landslide, and (

**d**) cracks in the steps.

**Figure 8.**Wanjiawan landslide cumulative displacement, rainfall, and groundwater level monitoring data.

**Figure 9.**ICEEMDAN decomposition term of total landslide displacement: (

**a**) IMF1, (

**b**) IMF2, (

**c**) IMF3, and (

**d**) R.

**Figure 10.**The trend and fluctuation displacements of D1: (

**a**) fluctuation item displacement and (

**b**) trend item displacement.

**Figure 11.**Relationship between the groundwater level, daily rainfall, and fluctuation term displacement changes.

**Figure 15.**The displacement prediction results of different combination neural networks with fluctuation terms.

Category | Candidate Triggering Factors |
---|---|

Displacement | Input 1: Displacement over the past one day |

Input 2: Displacement over the past two days | |

Input 3: Cumulative displacement of the previous day | |

Precipitation | Input 4: Cumulative rainfall of the day |

Input 5: Cumulative rainfall within two days | |

Input 6: Cumulative rainfall of the previous day | |

Groundwater level | Input 7: Daily groundwater level elevation |

Input 8: Change in groundwater level elevation in the past day | |

Input 9: Change in groundwater level elevation today | |

Input 10: Decrease in groundwater level changes compared to the previous day |

Evaluating Indicator | LSTM | BiLSTM | TCN | TCN–LSTM | TCN–BiLSTM |
---|---|---|---|---|---|

RMSE (mm) | 0.725 | 0.586 | 0.551 | 0.274 | 0.129 |

MAPE (%) | 0.433 | 0.354 | 0.305 | 0.120 | 0.033 |

R^{2} | 0.915 | 0.945 | 0.951 | 0.988 | 0.997 |

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## Share and Cite

**MDPI and ACS Style**

Lin, Q.; Yang, Z.; Huang, J.; Deng, J.; Chen, L.; Zhang, Y.
A Landslide Displacement Prediction Model Based on the ICEEMDAN Method and the TCN–BiLSTM Combined Neural Network. *Water* **2023**, *15*, 4247.
https://doi.org/10.3390/w15244247

**AMA Style**

Lin Q, Yang Z, Huang J, Deng J, Chen L, Zhang Y.
A Landslide Displacement Prediction Model Based on the ICEEMDAN Method and the TCN–BiLSTM Combined Neural Network. *Water*. 2023; 15(24):4247.
https://doi.org/10.3390/w15244247

**Chicago/Turabian Style**

Lin, Qinyue, Zeping Yang, Jie Huang, Ju Deng, Li Chen, and Yiru Zhang.
2023. "A Landslide Displacement Prediction Model Based on the ICEEMDAN Method and the TCN–BiLSTM Combined Neural Network" *Water* 15, no. 24: 4247.
https://doi.org/10.3390/w15244247