# Landslide Susceptibility Mapping Based on Information-GRUResNet Model in the Changzhou Town, China

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}. Changzhou town is located at the confluence of the Xunjiang and Guijiang Rivers, which form the Xijiang River after their confluence. This river traverses urban areas from west to east, and the Xunjiang, Guijiang, and Xijiang Rivers meet the Wuzhou. The terrain features are high on all sides and low in the middle, with hills accounting for more than 80% of the total area. There are many rolling hills and continuous mountains, with little flat land. The terrain slopes from north to south to the central Xijiang River and mainly consists of hills and platforms below 300 m above sea level. The Tropic of Cancer passes through Changzhou town. The area has a subtropical humid monsoon climate featuring strong solar radiation, abundant sunshine, warm temperatures, abundant rainfall, a short winter, and a long frost-free period. The average annual rainfall totals 1503.6 mm. The specific geographical location of Changzhou town is shown in Figure 1.

#### 2.2. Environmental Factors

#### 2.3. Process of Regional Landslide Susceptibility Mapping Based on the Information-GRUResNet Model

#### 2.4. Normalization

#### 2.5. Principal Component Analysis

#### 2.6. Information Theory

#### 2.7. GRU Model

#### 2.8. ResNetGRU Model

#### 2.9. Evaluation Index

## 3. Results

#### 3.1. Landslide Susceptibility Mapping Based on Information Theory

#### 3.2. Landslide Susceptibility Mapping with the Information-GRUResNet Model

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Chao, Z.; Kunlong, Y.; Ying, C.; Bayes, A. Application of time series analysis and PSO–SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China. Eng. Geol.
**2016**, 204, 108–120. [Google Scholar] - Yonggang, Z.; Jun, T.; Zhengying, H.; Junkun, T. A novel displacement prediction method using gated recurrent unit model with time series analysis in the Erdaohe landslide. Nat. Hazards
**2021**, 105, 783–813. [Google Scholar] - Xiuzhen, L.; Shengwei, L. Large-Scale Landslide Displacement Rate Prediction Based on Multi-Factor Support Vector Regression Machine. Appl. Sci.
**2021**, 11, 1381. [Google Scholar] - Shaohong, L.; Na, W. A new grey prediction model and its application in landslide displacement prediction. Chaos Solitons Fractals
**2021**, 147, 110969. [Google Scholar] - Yong, Z.; Zhengwei, Z.; Wanjie, L.D.G.; Wang, X. Experimental research on a novel optic fiber sensor based on OTDR for landslide monitoring. Measurement
**2019**, 148, 106926. [Google Scholar] - Junwei, M.; Huiming, T.; Xiao, L.; Tao, W.; Junrong, Z.; Qinwen, T.; Zhiqiang, F. Probabilistic forecasting of landslide displacement accounting for epistemic uncertainty: A case study in the Three Gorges Reservoir area, China. Landslides
**2018**, 15, 1145–1153. [Google Scholar] - Shiluo, X.; Ruiqing, N. Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China. Comput. Geosci.
**2018**, 111, 87–96. [Google Scholar] - Richard, M.; Lanhai, L.; Jean, B.N.; Christophe, M.; Enan, M.N.; Patient, M.K.; Aboubakar, G.; Egide, H. Landslide susceptibility and influencing factors analysis in Rwanda. Environ. Dev. Sustain.
**2020**, 22, 7985–8012. [Google Scholar] - Andrea, C.; Federico, R.; Daniela, L.; Filippo, C.; Nicola, C. Landslide susceptibility map refinement using PSInSAR data. Remote Sens. Environ.
**2016**, 184, 302–315. [Google Scholar] - Xing, A.-Z.; Yamin, M.; Rongxun, W.; Tongxin, Z.; Yongcui, D.; Junzhi, L.; Lin, Y.; Cheng-Zhi, Q.; Haoyuan, H. A comparative study of an expert knowledge-based model and two data-driven models for landslide susceptibility mapping. Catena
**2018**, 166, 317–327. [Google Scholar] - Rubini, M.; Olsen, J.M. Evaluation of the influence of source and spatial resolution of DEMs on derivative products used in landslide mapping. Geomat. Nat. Hazards Risk
**2015**, 7, 1835–1855. [Google Scholar] - Qingfeng, H.; Himan, S.; Ataollah, S.; Shaojun, L.; Wei, C.; Nianqin, W.; Huichan, C.; Huiyuan, B.; Jianquan, M.; Yingtao, C.; et al. Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms. Sci. Total Environ.
**2019**, 663, 1–15. [Google Scholar] - Vakhshoori, V.; Zare, M. Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods. Geomat. Nat. Hazards Risk
**2016**, 7, 1731–1752. [Google Scholar] [CrossRef] - Guo-liang, D.; Yong-shuang, Z.; Javed, I.; Zhi-hua, Y.; Xin, Y. Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China. J. Mt. Sci.
**2017**, 2, 249–268. [Google Scholar] - Haoyuan, H.; Paraskevas, T.; Ioanna, I.; Constantinos, L.; Yi, W. Introducing a novel multi-layer perceptron network based on stochastic gradient descent optimized by a meta-heuristic algorithm for landslide susceptibility mapping. Sci. Total Environ.
**2020**, 742, 140549. [Google Scholar] - Qian, W.; Yi, W.; Ruiqing, N.; Ling, P. Integration of Information Theory, K-Means Cluster Analysis and the Logistic Regression Model for Landslide Susceptibility Mapping in the Three Gorges Area, China. Remote Sens.
**2017**, 9, 938. [Google Scholar] - Taskin, K.; Emrehan, K.S.; Ismail, C. An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: A case study of Duzkoy district. Nat. Hazards
**2015**, 76, 471–496. [Google Scholar] - Wenhuan, W.; Qiang, Z.; Vijay, P.S.; Gang, W.; Jiaqi, Z.; Zexi, S.; Shuai, S. A Data-Driven Model on Google Earth Engine for Landslide Susceptibility Assessment in the Hengduan Mountains, the Qinghai–Tibetan Plateau. Remote Sens.
**2022**, 14, 4662. [Google Scholar] - Prakash, N.; Manconi, A.; Loew, S. Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models. Remote Sens.
**2020**, 12, 346. [Google Scholar] [CrossRef][Green Version] - Thomas, S.; Dalia, B.K. A heuristic approach to global landslide susceptibility mapping. Nat. Hazards
**2017**, 87, 145–164. [Google Scholar] - Jie, D.; Ali, P.Y.; Dieu, T.B.; Abdelaziz, M.; Mehebub, S.; Zhongfan, Z.; Chi-Wen, C.; Khabat, K.; Yong, Y.; Binh, T.P. Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Sci. Total Environ.
**2019**, 662, 332–346. [Google Scholar] - Vahid, M.; Yaghoub, N. Development of hybrid wavelet packet-statistical models (WP-SM) for landslide susceptibility mapping. Landslides
**2016**, 13, 97–114. [Google Scholar] - Wei, C.; Hamid, R.P.; Zhou, Z. A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping. Geocarto Int.
**2016**, 32, 367–385. [Google Scholar] - Wei, C.; Wenping, L.; Huichan, C.; Enke, H.; Xiaoqin, L.; Xiao, D. GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China. Environ. Earth Sci.
**2016**, 75, 63. [Google Scholar] - Xianyu, Y.; Kaixiang, Z.; Yingxu, S.; Weiwei, J.; Jianguo, Z. Study on landslide susceptibility mapping based on rock–soil characteristic factors. Sci. Rep.
**2021**, 11, 15476. [Google Scholar] - Paraskevas, T.; Ioanna, I. Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size. Catena
**2016**, 145, 164–179. [Google Scholar] - Mahdi, P.; Amiya, G.; Hamid, R.P.; Fatemeh, R.; Saro, L. Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms. Sci. Total Environ.
**2020**, 741, 139937. [Google Scholar] - Mohammad, F.; Alireza, V.; Ali, A.A.; Mahdi, M.; Hossein, A. Mapping landslide susceptibility in the Zagros Mountains, Iran: A comparative study of different data mining models. Earth Sci. Inform.
**2019**, 12, 615–628. [Google Scholar] - Krzysztof, G.; María, T.R. The importance of input data on landslide susceptibility mapping. Sci. Rep.
**2021**, 11, 19334. [Google Scholar] - Abdelaziz, M.; Ali, P.Y.; Jie, D.; Jim, W.; Binh, T.; Dieu, T.B.; Ram, A.; Boumezbeur, A. Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Sci. Rev.
**2020**, 207, 103225. [Google Scholar] - Faming, H.; Jing, Z.; Chuangbing, Z.; Yuhao, W.; Jinsong, H.; Li, Z. A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction. Landslides
**2020**, 17, 217–229. [Google Scholar] - Yanting, P.; Yaping, H.; Qi, Z.; Xingyuan, Z.; Song, W. Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification. IEEE Trans. Pattern Anal. Mach. Intell.
**2021**, 43, 1239–1253. [Google Scholar] - Yaning, Y.; Zhijie, Z.; Wanchang, Z.; Huihui, J.; Jianqiang, Z. Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: A case study in Jiuzhaigou region. Catena
**2020**, 195, 104851. [Google Scholar] - Zhice, F.; Yi, W.; Ling, P.; Haoyuan, H. Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping. Comput. Geosci.
**2020**, 139, 104470. [Google Scholar] - Somnath, B.; Vaibhav, K.U.; Balamurugan, G.; Thomas, O. Landslide inventory and susceptibility models considering the landslide typology using deep learning: Himalayas, India. Nat. Hazards
**2021**, 108, 1257–1289. [Google Scholar] - Weidong, W.; Zhuolei, H.; Zheng, H.; Yange, L.; Jie, D.; Jianling, H. Mapping the susceptibility to landslides based on the deep belief network: A case study in Sichuan Province, China. Nat. Hazards
**2020**, 103, 3239–3261. [Google Scholar] - Phuong, T.T.; Mahdi, P.; Khabat, K.; Omid, G.; Narges, K.; Artemi, C.; Saro, L. Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geosci. Front.
**2021**, 12, 505–519. [Google Scholar] - Maher, I.S.; Biswajeet, P.; Saro, L. Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment. Catena
**2020**, 186, 104249. [Google Scholar] - Faming, H.; Kunlong, Y.; Jinsong, H.; Lei, G.; Peng, W. Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine. Eng. Geol.
**2017**, 223, 11–22. [Google Scholar] - Ping, L.; Ruimao, Z.; Jiamin, R.; Zhanglin, P.; Jingyu, L. Switchable Normalization for Learning-to-Normalize Deep Representation. IEEE Trans. Pattern Anal. Mach. Intell.
**2019**, 43, 712–728. [Google Scholar] - Zhaodong, C.; Lei, D.; Guoqi, L.; Jiawei, S. Effective and Efficient Batch Normalization Using a Few Uncorrelated Data for Statistics Estimation. IEEE Trans. Neural Netw. Learn. Syst.
**2020**, 32, 348–362. [Google Scholar] - Furkan, E.; Özgün, Y.; Ali, Y.M. Predictive modeling of biomass gasification with machine learning-based regression methods. Energy
**2020**, 191, 116541. [Google Scholar] - Douglas, M.C.; Ana, P.F.; Brivaldo, G.A.; Fernando, J.F.; dos Santos Silva, T.H.; Silva Cavalcante, F.M. Physical soil quality indicators for environmental assessment and agricultural potential of Oxisols under different land uses in the Araripe Plateau, Brazil. Soil Tillage Res.
**2021**, 209, 104951. [Google Scholar] - Zhihua, Y.; Hengxing, L.; Xing, G.; Langping, L.; Yunshan, M.; Yuming, W. Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China. Nat. Hazards
**2015**, 75, 2467–2487. [Google Scholar] - Paraskevas, T.; Ioanna, I.; Haoyuan, H.; Wei, C.; Chong, X. Applying information theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China. Landslide
**2017**, 14, 1091–1111. [Google Scholar] - Hamid, R.P.; Majid, M.; Biswajeet, P. Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena
**2012**, 97, 71–84. [Google Scholar] - Krishna, C.D.; Amar, D.R.; Hamid, R.P.; Kohki, Y.; Biswajeet, P.; In, C.R.; Megh, R.D.; Omar, F. Althuwaynee. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Nat. Hazards
**2013**, 65, 135–165. [Google Scholar] - Chuxiong, H.; Tiansheng, O.; Yu, Z.; LiMin, Z. GRU-Type LARC Strategy for Precision Motion Control with Accurate Tracking Error Prediction. IEEE Trans. Ind. Electron.
**2020**, 68, 812–820. [Google Scholar] - Jin, W.; You, Z.; LiangChih, Y.; Xuejie, Z. Contextual sentiment embeddings via bi-directional GRU language model. Knowl.-Based Syst.
**2022**, 235, 107663. [Google Scholar] - Yaxiong, W.; Zhenhang, C.; Wei, Z. Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning. Energy
**2022**, 244, 123178. [Google Scholar] - Zhimin, L.; Shaobing, Y.; Yuanzhong, W.; Jinyu, Z. Discrimination of the fruits of Amomum tsao-ko according to geographical origin by 2DCOS image with RGB and Resnet image analysis techniques. Microchem. J.
**2021**, 169, 106545. [Google Scholar] - Stephan, F.; Jesse, G. Defining the extent of gene function using ROC curvature. Stephan Fischer. Bioinformatics
**2022**, 38, 5390–5397. [Google Scholar] - Sadatsafavi, M.; Saha-Chaudhuri, P.; Petkau, J. Model-Based ROC Curve: Examining the Effect of Case Mix and Model Calibration on the ROC Plot. Med. Decis. Mak.
**2022**, 42, 487–499. [Google Scholar] [CrossRef] - Lin, Z.; Sun, X.; Ji, Y. Landslide Displacement Prediction based on Time Series Analysis and Double-BiLSTM Model. Int. J. Enviromental Res. Public Health
**2022**, 19, 2077. [Google Scholar] [CrossRef] [PubMed] - Taylor, K.E. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. Atmos.
**2001**, 106, 7183–7192. [Google Scholar] [CrossRef] - Lucy, R.W.; Alessandra, M.; Malcolm, H.; Moninya, R.; Craig, R.S. Assessment of surface currents measured with high-frequency phased-array radars in two regions of complex circulation. IEEE J. Ocean. Eng.
**2017**, 43, 484–505. [Google Scholar]

**Figure 4.**Process of regional landslide susceptibility mapping based on the Information-GRUResNet model.

**Figure 10.**Comparison of the landslide susceptibility mapping results of Information-GRUResNet and the GRU, RF, and LR models.

**Figure 11.**Comparison of the ROC and AUC results for the Information-GRUResNet, GRU, RF, and LR models.

**Figure 12.**Comparison of the ROC and AUC results for the Information-GRUResNet, GRU, RF, and LR models.

Landform | Geological Structure | Lithological Class | Human Activity Level | Slope Type | Regional Slope |
---|---|---|---|---|---|

47.43% | 12.76% | 9.36% | 9.85% | 12.2% | 8.4% |

Environmental Factors | Number of Landslide Points | Total Number of Landslide Points | Number of Grids for Each Factor | Grid Area of Each Factor | Total Grid Area | Information Theory Value | Normalized Information Theory Value |
---|---|---|---|---|---|---|---|

Landform | |||||||

High hills | 8 | 65 | 200,519 | 180,467,100 | 309,358,800 | −1.55599245 | 0.073032586 |

Low mountains and shallow-cut terrain | 47 | 65 | 43,898 | 39,508,200 | 309,358,800 | 1.733753855 | 1 |

Undulating mounds | 8 | 65 | 34,353 | 30,917,700 | 309,358,800 | 0.208227205 | 0.570145123 |

Valley terraces | 2 | 65 | 64,962 | 58,465,800 | 309,358,800 | −1.81518028 | 0 |

Geological structure | |||||||

>500 | 50 | 65 | 243,989 | 219,590,100 | 309,358,800 | 0.080374877 | 0.729635056 |

250–500 | 4 | 65 | 48,020 | 43,218,000 | 309,358,800 | −0.81984821 | 0 |

100–250 | 5 | 65 | 30,749 | 27,674,100 | 309,358,800 | −0.15094454 | 0.542149574 |

<100 | 6 | 65 | 20,974 | 18,876,600 | 309,358,800 | 0.413950908 | 1 |

Lithological class | |||||||

Sandstone | 0 | 65 | 4903 | 4,412,700 | 309,358,800 | - | - |

Quaternary system | 7 | 65 | 76,970 | 69,273,000 | 309,358,800 | −0.73203057 | 0 |

Clastic rock | 51 | 65 | 225,559 | 203,003,100 | 309,358,800 | 0.178718881 | 1 |

Granite | 7 | 65 | 36,300 | 32,670,000 | 309,358,800 | 0.019567422 | 0.825252204 |

Human activity level | |||||||

Very weak | 1 | 65 | 86,154 | 77,535,600 | 309,358,800 | −2.79062268 | 0 |

Strong | 16 | 65 | 108,166 | 97,349,400 | 309,358,800 | −0.24560334 | 0.531037444 |

Moderate | 36 | 65 | 25,715 | 23,143,500 | 309,358,800 | 2.001919473 | 1 |

Weak | 12 | 65 | 123,697 | 111,327,300 | 309,358,800 | −0.66745336 | 0.443015263 |

Slope type | |||||||

Transverse slope | 26 | 65 | 108,546 | 97,691,400 | 309,358,800 | 0.236397506 | 0.705986485 |

Reverse slope | 6 | 65 | 38,431 | 34,587,900 | 309,358,800 | −0.19162994 | 0.347459581 |

Oblique slope | 15 | 65 | 80,151 | 72,135,900 | 309,358,800 | −0.01038714 | 0.499273258 |

Forward slope | 1 | 65 | 4397 | 3,957,300 | 309,358,800 | 0.184552524 | 0.662559782 |

Soil layer thickness <2 m | 5 | 65 | 48,490 | 43,641,000 | 309,358,800 | −0.60644466 | 0 |

Soil layer thickness 2–4 m | 6 | 65 | 44,161 | 39,744,900 | 309,358,800 | −0.33060756 | 0.231048318 |

Soil layer thickness 4–6 m | 0 | 65 | 1922 | 1,729,800 | 309,358,800 | - | - |

Soil layer thickness >6 m | 6 | 65 | 17,634 | 15,870,600 | 309,358,800 | 0.587405627 | 1 |

Regional slope | |||||||

<15 | 15 | 65 | 100,225 | 90,202,500 | 309,358,800 | −0.23389244 | 0.583159479 |

25–35 | 5 | 65 | 75,284 | 67,755,600 | 309,358,800 | −1.04635470 | 0 |

>=35 | 45 | 65 | 168,222 | 151,399,800 | 309,358,800 | 0.346852968 | 1 |

15–25 | 0 | 65 | 0 | 900 | 309,358,800 | - | - |

**Table 3.**The number of landslides corresponding to different susceptibility levels determined based on the information theory method.

Susceptibility Level | Number of Grids in the Region | Proportion of Grids (%) | Number of Landslides | Proportion of Landslides (%) |
---|---|---|---|---|

Very high | 74,118 | 21.56 | 39 | 60 |

High | 101,529 | 29.54 | 14 | 21.54 |

Moderate | 76,771 | 22.33 | 7 | 10.77 |

Low | 70,866 | 20.62 | 4 | 6.15 |

Very low | 20,448 | 5.95 | 1 | 1.54 |

**Table 4.**The number of landslides of different susceptibility levels estimated with the Information-GRUResNet model.

Susceptibility Level | Number of Grids in the Region | Proportion of Grids (%) | Number of Landslides | Proportion of Landslides (%) |
---|---|---|---|---|

Very high | 74,118 | 21.56 | 39 | 60 |

High | 101,529 | 29.54 | 14 | 21.54 |

Moderate | 76,771 | 22.33 | 7 | 10.77 |

Low | 70,866 | 20.62 | 4 | 6.15 |

Very low | 20,448 | 5.95 | 1 | 1.54 |

**Table 5.**Number of landslides in different susceptibility levels for the Information-GRUResNet, GRU, RF, and LR models.

Susceptibility Level | Information-GRUResNet | GRU | RF | LR |
---|---|---|---|---|

Very high | 39 | 13 | 24 | 15 |

High | 14 | 33 | 14 | 34 |

Moderate | 7 | 11 | 17 | 7 |

Low | 4 | 6 | 6 | 8 |

Very low | 1 | 2 | 0 | 1 |

Model | MSE | MAE | RMSE |
---|---|---|---|

Information-GRUResNet | 1.32307692 | 0.83076923 | 1.15025081 |

GRU | 1.67692308 | 1.01 | 1.29496065 |

RF | 2.13846154 | 1.09230769 | 1.46234795 |

LR | 1.89230769 | 1.03076923 | 1.37561175 |

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## Share and Cite

**MDPI and ACS Style**

Lin, Z.; Chen, Q.; Lu, W.; Ji, Y.; Liang, W.; Sun, X.
Landslide Susceptibility Mapping Based on Information-GRUResNet Model in the Changzhou Town, China. *Forests* **2023**, *14*, 499.
https://doi.org/10.3390/f14030499

**AMA Style**

Lin Z, Chen Q, Lu W, Ji Y, Liang W, Sun X.
Landslide Susceptibility Mapping Based on Information-GRUResNet Model in the Changzhou Town, China. *Forests*. 2023; 14(3):499.
https://doi.org/10.3390/f14030499

**Chicago/Turabian Style**

Lin, Zian, Qiuguang Chen, Weiping Lu, Yuanfa Ji, Weibin Liang, and Xiyan Sun.
2023. "Landslide Susceptibility Mapping Based on Information-GRUResNet Model in the Changzhou Town, China" *Forests* 14, no. 3: 499.
https://doi.org/10.3390/f14030499