Welcome to Scholar Publishing Group

International Journal of Engineering Technology and Construction, 2023, 4(1); doi: 10.38007/IJETC.2023.040103.

Geological Hazard Prediction of Regional Landslides Based on Geological Clouds and Meteorological Data


Li Wang

Corresponding Author:
Li Wang

Northwest Minzu University, Lanzhou, China


Landslide disasters are extremely harmful geological events. The occurrence of landslides will pose a great threat to human life and property safety, and will also cause huge damage to the environment and ecology, restricting human sustainable development. The survey data shows that the proportion of landslide disasters in China ranks first among all geological disasters, accounting for 74%. Therefore, it is of great economic value and social significance to take necessary and effective landslide disaster assessment studies to effectively predict landslide disasters. After decades of scientific exploration, China has made gratifying results in the study of landslide disasters, but there are still some problems in the quantification of the self-organized criticality of regional geological disasters, frequency and spatial dependence; The selection of evaluation factors in the analysis of landslide disaster susceptibility, the limit of risk assessment and susceptibility analysis also need to continue to explore. The purpose of this article is to predict regional landslide geohazards based on geological clouds and meteorological data. This paper fully absorbs the core idea of Logistical and solves the problem of sample quantification in the process of evaluation and prediction of large regions. Based on the comprehensive advantages of GIS technology and Logistical method, a risk assessment model is established. Taking Xinjiang landslide disaster as an example, the danger of landslide disaster is evaluated. This article concludes that the analysis of the landslide disaster in Xinjiang shows that when the cumulative landslide displacement is between 0. 2 and 0. 4m, the landslide is in a critical state, and an orange warning message is issued. When the cumulative landslide displacement value is greater than 0. 4m, the landslide is in danger. Status, release red warning information.


Landslide Disaster, Landslide Disaster Assessment, Evaluation Factor, Disaster Prediction

Cite This Paper

Li Wang. Geological Hazard Prediction of Regional Landslides Based on Geological Clouds and Meteorological Data. International Journal of Engineering Technology and Construction (2023), Vol. 4, Issue 1: 31-47. https://doi.org/10.38007/IJETC.2023.040103.


[1] Dingde Xu, Li Peng, Shaoquan Liu. Influences of Risk Perception and Sense of Place on Landslide Disaster Preparedness in Southwestern China. International Journal of Disaster Risk Science, 2018, 9(2):1-14. 

[2] Ari J. Posner, Konstantine P. Georgakakos. Soil moisture and precipitation thresholds for real-time landslide prediction in El Salvador. Landslides, 2015, 12(6):1179-1196. 

[3] Peng Ling, Xu Suning, Peng Junhuan. Regional Landslide Risk Assessment Using Multi-Source Remote Sensing Data. Journal of Jilin University, 2016, 46(1):175-186. 

[4] W. Sai, X. Suning, P. Ling. A rapid extraction of landslide disaster information research based on GF-1 image. Proceedings of SPIE - The International Society for Optical Engineering, 2015, 9669(1):307-314. 

[5] H Tjahjono, S Suripin, K Kismartini. Structuring the Environment of Landslide-Prone Disaster and Its Mitigation in The District of Banyumanik. Iop Conference, 2018, 145(1):012082. 

[6] H Habil, E Yuliza, M M Munir. Instrumentation system design and laboratory scale simulation of landslide disaster mitigation. Journal of Physics Conference, 2016, 739(1):012056. 

[7] LI Xishuang, LIU Baohua, LIU Lejun. Prediction for Potential Landslide Zones Using Seismic Amplitude in Liwan Gas Field, Northern South China Sea. Journal of Ocean University of China, 2017(6):1035-1042. 

[8] Alessandro Messeri, Marco Morabito, Gianni Messeri. Weather-Related Flood and Landslide Damage: A Risk Index for Italian Regions. Plos One, 2015, 10(12):e0144468. 

[9] Pei Zuan, Yong Huang. Prediction of Sliding Slope Displacement Based on Intelligent Algorithm. Wireless Personal Communications, 2018, 102(3):1-17. 

[10] Abolfazl Jaafari. LiDAR-supported prediction of slope failures using an integrated ensemble weights-of-evidence and analytical hierarchy process. Environmental Earth Sciences, 2018, 77(2):42. 

[11] Marcos Barreto de Mendonca, Adriana Sobreira Valois. Disaster education for landslide risk reduction: an experience in a public school in Rio de Janeiro State, Brazil. Natural Hazards, 2017, 89(1):351-365. 

[12] Joko Sidik Purnomo, Yusep Muslih Purwana, Niken Silmi Surjandari. Analysis of slope slip surface case study landslide road segment Purwantoro-Nawangan/Bts Jatim Km 89+400. Journal of Physics Conference, 2017, 795(1):012069. 

[13] Y. Chen, Tianbin Li, Y. Wei. Mechanism of gorge-type landslide disaster chain and its hazard evaluation. Chinese Journal of Rock Mechanics & Engineering, 2016, 35(s2):4073-4081. 

[14] Chi-Wen Chen, Takashi Oguchi, Yuichi S. Hayakawa. Relationship between landslide size and rainfall conditions in Taiwan. Landslides, 2017, 14(3):1235-1240. 

[15] Q. Xue, M. Zhang. Monitoring, early warning and deformation characteristics of yantu'an landslide in Yan'an. Northwestern Geology, 2018, 51(2):220-226. 

[16] D. B. Kirschbaum, T. Stanley, J. Simmons. A dynamic landslide hazard assessment system for Central America and Hispaniola. Natural Hazards & Earth System Sciences Discussions, 2015, 15(10):2847-2882. 

[17] Haoyuan Hong, Biswajeet Pradhan, Maher Ibrahim. Improving the accuracy of landslide susceptibility model using a novel region-partitioning approach. Landslides, 2018(1):1-20. 

[18] N. Liu, L. Hu, G. Chen. Automatic monitoring and result analysis of primary school landslide in three gorges reservoir area. Journal of Geomatics, 2018, 43(4):41-44. 

[19] C. -H. Zhang, M. -Z. Chen, R. -B. Zheng. Landslide hazard risk assessment and zoning of Huadu district of guangzhou based on "3S” technique and logistic regress-weighted SVM model. Journal of Ecology & Rural Environment, 2015, 31(6):955-962. 

[20] Tian Qinghuai, Lin Jinhui, Wei Shengli. Measures and effects of comprehensive control of landslide:A case study of Yejialu slope of Jiande City, Zhejiang Province. Science of Soil & Water Conservation, 2015, 13(2):118-121. 

[21] Christos Chalkias, Christos Polykretis, Maria Ferentinou. Integrating Expert Knowledge with Statistical Analysis for Landslide Susceptibility Assessment at Regional Scale. Geosciences, 2016, 6(1):14. 

[22] Jorge Alán Salinas-Jasso, Luis G. Ramos-Zuñiga, Juan C. Montalvo〢rrieta. Regional landslide hazard assessment from seismically induced displacements in Monterrey Metropolitan Area, Northeastern Mexico. Bulletin of Engineering Geology & the Environment, 2019, 78(2):1127-1141. 

[23] J. C. Robbins, M. G. Petterson. Landslide inventory development in a data sparse region: spatial and temporal characteristics of landslides in Papua New Guinea. Natural Hazards & Earth System Sciences Discussions, 2015, 3(8):4871-4917. 

[24] P. T. Ghazvinei, J. Zandi, J. Ariffin. Approaches for delineating landslide hazard areas using receiver operating characteristic in an advanced calibrating precision soil erosion model. Natural Hazards & Earth System Sciences Discussions, 2015, 3(10):6321-6349. 

[25] Stefano Luigi Gariano, Olga Petrucci, Guido Rianna. Impacts of past and future land changes on landslides in southern Italy. Regional Environmental Change, 2017, 18(2):437–449. 

[26] Hsiao-Wei Chung, Cheng-Chien Liu, I-Fan Cheng. Rapid Response to a Typhoon-Induced Flood with an SAR-Derived Map of Inundated Areas: Case Study and Validation. Remote Sensing, 2015, 7(9):11954-11973. 

[27] Jewgenij Torizin, Michael Fuchs, Adnan Alam Awan. Statistical landslide susceptibility assessment of the Mansehra and Torghar districts, Khyber Pakhtunkhwa Province, Pakistan. Natural Hazards, 2017, 89(4):757-784. 

[28] A Latypov, N Zharkova, I Nuriyev. Landslide hazard assessment in city under construction Innopolis (Russia). IOP Conference Series Earth and Environmental Science, 2016, 33(1):012042.