According to the public data, the overall trend of economic loss caused by landslide disaster is growing year by year, and improving the prediction level of landslide disaster can effectively reduce the loss caused by landslide disaster. Therefore, the study of landslide prediction and prediction has important practical significance, and landslide displacement prediction is an important content of landslide prediction and prediction. This paper mainly studies landslide risk assessment based on artificial neural network model. In this paper, based on the results of field investigation, literature summary and expert experience, collected samples of 100 landslides, analyzes the distribution and the main influence factors of landslide, and establishes the surface elevation, and vegetation index, cutting slope, annual average rainfall, surface density, overlying soil types for the impact factor of regional landslide hazard assessment system. According to the requirement of BP neural network (BPNN) for input data, the landslide sample data processing method which is suitable for Sichuan landslide risk assessment system is established.

Khadijah Ragab. Landslide Risk Assessment Based on Artificial Neural Network Model. Machine Learning Theory and Practice (2021), Vol. 2, Issue 3: 44-51. https://doi.org/10.38007/ML.2021.020306.

[1] Asghari S, Balvasi I. A Comparison of Artificial Neural Network Model with Fuzzy logic model In Landslide Hazard Assessment. Quantitative Geomorphological Research, 2018, 7(2): 158-182.

[2] Rajabi A M, Khodaparast M, Mohammadi M. Earthquake-induced landslide prediction using back-propagation type artificial neural network: case study in northern Iran. Natural Hazards, 2021, 110(1): 679-694. https://doi.org/10.1007/s11069-021-04963-8

[3] Hammad M, Van Leeuwen B, Mucsi L. Integration of gis and advanced remote sensing techniques for landslide hazard assessment: A case study of northwest syria. Isprs Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2020, 6(6): 27-34. https://doi.org/10.5194/isprs-annals-VI-3-W1-2020-27-2020

[4] Lee J H, Kim H, Park H J, et al. Temporal prediction modeling for rainfall-induced shallow landslide hazards using extreme value distribution. Landslides, 2021, 18(1): 321-338. https://doi.org/10.1007/s10346-020-01502-7

[5] Aslam B, Zafar A, Khalil U. Development of integrated deep learning and machine learning algorithm for the assessment of landslide hazard potential. Soft Computing, 2021, 25(21): 13493-13512. https://doi.org/10.1007/s00500-021-06105-5

[6] Ngo P T T, Panahi M, Khosravi K, et al. Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geoscience Frontiers, 2021, 12(2): 505-519. https://doi.org/10.1016/j.gsf.2020.06.013

[7] Huqqani I A, Tien T L, Mohamad-Saleh J. Landslide hazard analysis using a multilayered approach based on various input data configurations. Geosfera Indonesia, 2021, 6(1): 20-39. https://doi.org/10.19184/geosi.v6i1.23347

[8] Panchal S, Shrivastava A K. Landslide hazard assessment using analytic hierarchy process (AHP): A case study of National Highway 5 in India. Ain Shams Engineering Journal, 2021, 13(3): 101626. https://doi.org/10.1016/j.asej.2021.10.021

[9] Thennavan E, Pattukandan Ganapathy G. Evaluation of landslide hazard and its impacts on hilly environment of the Nilgiris District-a geospatial approach. Geoenvironmental Disasters, 2020, 7(1): 1-14. https://doi.org/10.1186/s40677-019-0139-3

[10] Hejazi A, Rezaeimoghaddam M, Naseri A. Landslide hazard zoning using artificial neural network models and TOPSIS downstream of Sanandaj Dam. Hydrogeomorphology, 2020, 7(24): 65-82.

[11] Karami M. Combination AHP and Neural Network Model to landslide Hazard Zonation (Case Study city of Bijar). Journal of Engineering Geology, 2018, 12(1): 153-182.

[12] Lee S. Current and future status of GIS-based landslide susceptibility mapping: a literature review. Korean Journal of Remote Sensing, 2019, 35(1): 179-193.

[13] Lucchese L V, de Oliveira G G, Pedrollo O C. Attribute selection using correlations and principal components for artificial neural networks employment for landslide susceptibility assessment. Environmental monitoring and assessment, 2020, 192(2): 1-22. https://doi.org/10.1007/s10661-019-7968-0

[14] Nguyen H D, Pham V D, Nguyen Q H, et al. An optimal search for neural network parameters using the Salp swarm optimization algorithm: a landslide application. Remote Sensing Letters, 2020, 11(4): 353-362. https://doi.org/10.1080/2150704X.2020.1716409

[15] Ghorbanzadeh O, Blaschke T, Gholamnia K, et al. Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sensing, 2019, 11(2): 196. https://doi.org/10.3390/rs11020196

[16] Jacinth Jennifer J, Saravanan S. Artificial neural network and sensitivity analysis in the landslide susceptibility mapping of Idukki district, India. Geocarto International, 2021, 37(19): 5693-5715. https://doi.org/10.1080/10106049.2021.1923831

[17] Mallick J, Alqadhi S, Talukdar S, et al. Risk assessment of resources exposed to rainfall induced landslide with the development of GIS and RS based ensemble metaheuristic machine learning algorithms. Sustainability, 2021, 13(2): 457. https://doi.org/10.3390/su13020457

[18] Nayek P S, Gade M. Artificial neural network-based fully data-driven models for prediction of newmark sliding displacement of slopes. Neural Computing and Applications, 2021, 34(11): 9191-9203.