A Scientometric Analysis of Predicting Methods for Identifying the Environmental Risks Caused by Landslides
Source abstract
Abstract
The effects of a landslide can represent a very big problem, including the death of people, damage to the land, environmental pollution and the loss of natural resources. Landslides are the most important medium for transferring sediments and polluting waterways by earth and organic materials. An excess of sediments reduces the quality of fish habitat and the potability of water. In order to understand landslides in depth, a thorough study was conducted using a scientometric analysis, as well as a thorough practical examination of landslide analysis and monitoring techniques. This review focused on methods used for landslide analysis, including physical models requiring easily prepared event-based landslide inventory, probabilistic methods which are useful for both shallow and earthquake-based landslides, and landslide monitoring performed by remote sensing techniques, which provide data helpful for prediction, monitoring and mapping. The fundamental principles of each method are described in terms of the method used, and its advantages, and limits. People and infrastructure are at danger from landslides caused by heavy rain, so this report highlights landslide-prone regions and considers the analysis methods for landslides used in these countries, with a view to identifying mitigation measures for coping with landslide risks in hilly areas. Furthermore, future landslide research possibilities, as well as possible modeling methods, are addressed. The report summarizes some landslide prediction and monitoring techniques used in landslide-prone countries which can help inform researchers seeking to protect the public from danger in landslide areas.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Landslides and related hazards · Flood Risk Assessment and Management · Tree Root and Stability Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Civil Engineering, Wuhan University, Wuhan 430072, China
Department of Civil and Environmental Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA
References (100+)
Show source-provided references
1. Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography10.1177/0309133313515293 ↗
2. Simplified quantitative risk assessment of rainfall-induced landslides modelled by infinite slopes10.1016/j.enggeo.2014.06.024 ↗
3. Comparison of the presence-only method and presence-absence method in landslide susceptibility mapping10.1016/j.catena.2018.07.012 ↗
4. Effects of gravel on infiltration, runoff, and sediment yield in landslide deposit slope in Wenchuan earthquake area, China10.1007/s11356-016-6394-x ↗
5. Global patterns of loss of life from landslides10.1130/G33217.1 ↗
6. Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications10.1016/j.geomorph.2012.08.021 ↗
7. Effects of rainfall on surface environment and morphological characteristics in the Loess Plateau10.1007/s11356-020-10365-3 ↗
8. Landslide prediction, monitoring and early warning: A concise review of state-of-the-art10.1007/s12303-017-0034-4 ↗
9. Zhang, H., Huang, C., Hu, X., Mei, H., and Hu, R. (2022). Evaluating water resource carrying capacity using the deep learning method: A case study of Yunnan, Southwest China. Environ. Sci. Pollut. Res., 1–15.10.1007/s11356-022-19330-8 ↗
10. Catchment-scale impacts of shallow landslides on stream water chemistry10.1016/j.scitotenv.2022.153970 ↗
11. Geertsema, M., Highland, L., and Vaugeouis, L. (2009). Environmental impact of landslides. Landslides–Disaster Risk Reduction, Springer.10.1007/978-3-540-69970-5_31 ↗
12. Assessment of shallow landslide risk mitigation measures based on land use planning through probabilistic modelling10.1007/s10346-014-0478-9 ↗
13. Comparative analysis of two rainfall retrieval algorithms during extreme rainfall event: A case study on cloudburst, 2010 over Ladakh (Leh), Jammu and Kashmir10.1007/s11069-019-03714-0 ↗
14. A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas10.1016/j.enggeo.2006.03.004 ↗
15. Combining neural network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslides10.1007/s11069-011-9847-z ↗
16. Soil characterization for shallow landslides modeling: A case study in the Northern Apennines (Central Italy)10.1007/s10346-017-0809-8 ↗
17. Nsengiyumva, J.B., Nahayo, L., Huang, X., and Cai, P. (2018). Landslide susceptibility assessment using spatial multi-criteria evaluation model in Rwanda. Int. J. Environ. Res. Public Health, 15.10.3390/ijerph15020243 ↗
18. Chen, W., Pourghasemi, H.R., Kornejady, A., and Xie, X. (2019). GIS-based landslide susceptibility evaluation using certainty factor and index of entropy ensembled with alternating decision tree models. Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques, Springer.10.1007/978-3-319-73383-8_10 ↗
19. Wu, Y., Tian, A., and Lan, H. (2022). Comparisons of Dynamic Landslide Models on GIS Platforms. Appl. Sci., 12.10.3390/app12063093 ↗
20. Tekin, S., and Çan, T. (2022). Slide type landslide susceptibility assessment of the Büyük Menderes watershed using artificial neural network method. Environ. Sci. Pollut. Res., 1–15.10.21203/rs.3.rs-787100/v1 ↗
21. Acid rain: An unsuspected factor predisposing Panzhihua airport landslide, China10.1007/s11356-021-13308-8 ↗
22. Scaling properties of rainfall induced landslides predicted by a physically based model10.1016/j.geomorph.2013.12.039 ↗
23. Impact of uncertainty in rainfall estimation on the identification of rainfall thresholds for debris flow occurrence10.1016/j.geomorph.2014.06.015 ↗
24. Endo, T. (2022, March 29). Probable Distribution of the Amount of Rainfall Causing Landslides. Available online: https://agris.fao.org/agris-search/search.do?recordID=US201300555779.
25. The formation and development of debris flows in large watersheds after the 2008 Wenchuan Earthquake10.1007/s10346-014-0541-6 ↗
26. An integrated user-friendly ArcMAP tool for bivariate statistical modelling in geoscience applications10.5194/gmd-8-881-2015 ↗
27. Landslides in a changing climate10.1016/j.earscirev.2016.08.011 ↗
28. Landslide hazard assessment: Summary review and new perspectives10.1007/s100640050066 ↗
29. An advanced process-based distributed model for the investigation of rainfall-induced landslides: The effect of process representation and boundary conditions10.1002/2015WR016909 ↗
30. Identification and mapping of recent rainfall-induced landslides using elevation data collected by airborne Lidar10.5194/nhess-7-637-2007 ↗
31. Cary, M., and Rockwell, T. (2020). International collaboration in open access publications: How income shapes international collaboration. Publications, 8.10.3390/publications8010013 ↗
32. Assessment of rainfall-induced shallow landslide susceptibility using a GIS-based probabilistic approach10.1016/j.enggeo.2013.04.011 ↗
33. Physically-based and distributed approach to analyze rainfall-triggered landslides at watershed scale10.1016/j.geomorph.2011.03.019 ↗
34. Lacerda, W.A., Erlich, M., Fontoura, S.A.B., and Sayao, A.S.F. (2004). Modeling time-dependent areal slope stability. Landslides-Evaluation and Stabilization, Proceedings of the 9th International Symposium on Landslides, Rio de Janeiro, Brazil, 28 June–2 July 2004, AA Balkema Publishers.
35. Recommendations for the quantitative analysis of landslide risk
36. Mavroulis, S., Diakakis, M., Kranis, H., Vassilakis, E., Kapetanidis, V., Spingos, I., Kaviris, G., Skourtsos, E., Voulgaris, N., and Lekkas, E. (2022). Inventory of Historical and Recent Earthquake-Triggered Landslides and Assessment of Related Susceptibility by GIS-Based Analytic Hierarchy Process: The Case of Cephalonia (Ionian Islands, Western Greece). Appl. Sci., 12.10.3390/app12062895 ↗
37. A hybrid physical and maximum-entropy landslide susceptibility model10.3390/e17064271 ↗
38. Miao, Z., Peng, R., Wang, W., Li, Q., Chen, S., Zhang, A., Pu, M., Li, K., Liu, Q., and Hu, C. (2022). Integrating Data Modality and Statistical Learning Methods for Earthquake-Induced Landslide Susceptibility Mapping. Appl. Sci., 12.10.3390/app12031760 ↗
39. Comparative analysis of SHALSTAB and SINMAP for landslide susceptibility mapping in the Cunha River basin, southern Brazil10.1007/s11368-014-0886-4 ↗
40. GIS-based landslide susceptibility mapping using information, frequency ratio, and artificial neural network methods in Qinghai Province, Northwestern China10.1155/2021/4758062 ↗
41. Physically based and distributed rainfall intensity and duration thresholds for shallow landslides10.1007/s10346-020-01481-9 ↗
42. TRIGRS—A Fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis
43. Deng, Y.-C., Hwang, J.-H., and Lyu, Y.-D. (2021). Developing Real-Time Nowcasting System for Regional Landslide Hazard Assessment under Extreme Rainfall Events. Water, 13.10.3390/w13050732 ↗
44. Regional-scale simulation of flowslide triggering in stratified deposits10.1016/j.enggeo.2021.106248 ↗
45. Evaluation of TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis)’s predictive skill for hurricane-triggered landslides: A case study in Macon County, North Carolina10.1007/s11069-010-9670-y ↗
46. Physically-based landslide prediction over a large region: Scaling low-resolution hydrological model results for high-resolution slope stability assessment10.1016/j.envsoft.2019.104607 ↗
47. Mapping susceptibility of rainfall-triggered shallow landslides using a probabilistic approach10.1007/s00254-007-1042-x ↗
48. Assessment of rainfall-induced landslide susceptibility at the regional scale using a physically based model and fuzzy-based Monte Carlo simulation10.1007/s10346-018-01125-z ↗
49. Castañeda, C., Dibucho, K., Arauzo, L., and Raymundo, C. (2021, January 28–30). Calculation of the Probability of Landslides Caused by Precipitation Applying the Janbu and MonteCarlo Method in Skarn-Type Mineral Deposits. Proceedings of the International Conference on Human Interaction and Emerging Technologies, Strasbourg, France.10.1007/978-3-030-85540-6_79 ↗
50. Research on progressive failure process of Baishuihe landslide based on Monte Carlo model10.1007/s00477-016-1224-8 ↗
51. Lee, S., Jang, J., Kim, Y., Cho, N., and Lee, M.J. (2020). Susceptibility analysis of the Mt. Umyeon landslide area using a physical slope model and probabilistic method. Remote Sens., 12.10.3390/rs12162663 ↗
52. Probabilistic simulation of entire process of rainfall-induced landslides using random finite element and material point methods with hydro-mechanical coupling10.1016/j.compgeo.2020.103989 ↗
53. Early warning monitoring of natural and engineered slopes with ground-based synthetic-aperture radar10.1007/s00603-014-0554-4 ↗
54. Bardi, F., Raspini, F., Ciampalini, A., Kristensen, L., Rouyet, L., Lauknes, T.R., Frauenfelder, R., and Casagli, N. (2016). Space-borne and ground-based InSAR data integration: The Åknes test site. Remote Sens., 8.10.3390/rs8030237 ↗
55. A physics-based probabilistic forecasting model for rainfall-induced shallow landslides at regional scale10.5194/nhess-18-969-2018 ↗
56. A web-based GPS system for displacement monitoring and failure mechanism analysis of reservoir landslide10.1038/s41598-017-17507-7 ↗
57. Long-term InSAR, borehole inclinometer, and rainfall records provide insight into the mechanism and activity patterns of an extremely slow urbanized landslide10.1007/s10346-019-01276-7 ↗
58. Zhao, C., and Lu, Z. (2018). Remote sensing of landslides—A review. Remote Sens., 10.10.3390/rs10020279 ↗
59. Landslide mapping with remote sensing: Challenges and opportunities10.1080/01431161.2019.1672904 ↗
60. Shahzad, N., Ding, X., and Abbas, S. (2022). A Comparative Assessment of Machine Learning Models for Landslide Susceptibility Mapping in the Rugged Terrain of Northern Pakistan. Appl. Sci., 12.10.3390/app12052280 ↗
61. Donati, D., Rabus, B., Engelbrecht, J., Stead, D., Clague, J., and Francioni, M. (2021). A Robust SAR Speckle Tracking Workflow for Measuring and Interpreting the 3D Surface Displacement of Landslides. Remote Sens., 13.10.3390/rs13153048 ↗
62. Xu, Q., Guo, C., Dong, X., Li, W., Lu, H., Fu, H., and Liu, X. (2021). Mapping and Characterizing Displacements of Landslides with InSAR and Airborne LiDAR Technologies: A Case Study of Danba County, Southwest China. Remote Sens., 13.10.3390/rs13214234 ↗
63. Extreme rainfall and vulnerability assessment: Case study of Uttarakhand rivers10.1007/s11069-019-03765-3 ↗
64. Bopche, L., and Rege, P.P. (2021). Feature-based model for landslide prediction using remote sensing and digital elevation data. Advances in Signal and Data Processing, Springer.10.1007/978-981-15-8391-9_22 ↗
65. Dikshit, A., Sarkar, R., Pradhan, B., Segoni, S., and Alamri, A.M. (2020). Rainfall induced landslide studies in Indian Himalayan region: A critical review. Appl. Sci., 10.10.3390/app10072466 ↗
66. Sarkar, S. (2018). Landslide risk assessment in parts of the Darjeeling Himalayas, India. Integrating Disaster Science and Management, Elsevier.10.1016/B978-0-12-812056-9.00007-5 ↗
67. Application of statistical probabilistic methods in landslide susceptibility assessment in Kurseong and its surrounding area of Darjeeling Himalayan, India: RS-GIS approach10.1007/s10668-020-00783-1 ↗
68. Identification of landslide-prone slopes at Paglajhora area, Darjeeling Himalaya, India10.1007/s10346-020-01472-w ↗
69. Runout modeling and calibration of friction parameters of Kurichermala debris flow, India10.1007/s10346-020-01540-1 ↗
70. Rapid mapping of landslides in the western ghats (India) triggered by 2018 extreme monsoon rainfall using a deep learning approach10.1007/s10346-020-01602-4 ↗
71. Weathering and landslide occurrences in parts of Western Ghats, Kerala10.1007/s12594-011-0089-1 ↗
72. Landslides susceptibility assessment by analytical network process: A case study for Kuttiyadi river basin (Western Ghats, southern India)10.1007/s42452-020-03574-5 ↗
73. Evaluating pedogenesis and soil Atterberg limits for inducing landslides in the Western Ghats, Idukki District of Kerala, South India10.1007/s11069-020-04472-0 ↗
74. Identification of landslide susceptible villages around Kalsubai region, Western Ghats of Maharashtra using geospatial techniques10.1007/s12594-017-0718-4 ↗
75. Geomorphic appraisal of landslides on the windward slope of Western Ghats, southern India10.1007/s11069-014-1358-2 ↗
76. Landslide susceptibility mapping of Darjeeling Himalaya, India using index of entropy (IOE) model10.1007/s12518-018-0248-9 ↗
77. Landslide susceptibility zonation mapping: A case study from Darjeeling District, eastern Himalayas, India10.1007/s12524-018-0916-6 ↗
78. Landslide susceptibility assessment using information value method in parts of the Darjeeling Himalayas10.1007/s12594-013-0162-z ↗
79. Estimating rainfall threshold and temporal probability for landslide occurrences in Darjeeling Himalayas10.1007/s12303-020-0001-3 ↗
80. Abraham, M.T., Satyam, N., Pradhan, B., and Alamri, A.M. (2020). IoT-based geotechnical monitoring of unstable slopes for landslide early warning in the Darjeeling Himalayas. Sensors, 20.10.3390/s20092611 ↗
81. Developing a prototype landslide early warning system for Darjeeling Himalayas using SIGMA model and real-time field monitoring10.1007/s12303-021-0026-2 ↗
82. Integrating the analytical hierarchy process (AHP) and the frequency ratio (FR) model in landslide susceptibility mapping of Shiv-khola watershed, Darjeeling Himalaya10.1007/s13753-013-0021-y ↗
83. Integrating spatial, temporal, and magnitude probabilities for medium-scale landslide risk analysis in Darjeeling Himalayas, India10.1007/s10346-011-0304-6 ↗
84. Abraham, M.T., Satyam, N., Pradhan, B., and Alamri, A.M. (2020). The selection of rain gauges and rainfall parameters in estimating intensity-duration thresholds for landslide occurrence: Case study from Wayanad (India). Water, 12.10.3390/w12041000 ↗
85. Pepe, G., Mandarino, A., Raso, E., Cevasco, A., Firpo, M., and Casagli, N. (2018, January 17–21). Extreme flood and landslides triggered in the Arroscia Valley (Liguria Region, Northwestern Italy) during the November 2016 rainfall event. Proceedings of the IAEG/AEG Annual Meeting Proceedings, San Francisco, CA, USA.10.1007/978-3-319-93124-1_21 ↗
86. Residual mechanisms and kinematics of the relict Lemeglio coastal landslide (Liguria, northwestern Italy)10.1016/j.geomorph.2018.08.010 ↗
87. Shallow landslide susceptibility, Rupinaro catchment, Liguria (northwestern Italy)10.1080/17445647.2019.1593252 ↗
88. A multidisciplinary approach for landslide residual risk assessment: The Pomarico landslide (Basilicata Region, Southern Italy) case study10.1007/s10346-020-01526-z ↗
89. Landslide inventory of the Basilicata region (Southern Italy)10.1080/17445647.2018.1475309 ↗
90. Caporossi, P., Mazzanti, P., and Bozzano, F. (2018). Digital image correlation (DIC) analysis of the 3 December 2013 Montescaglioso landslide (Basilicata, southern Italy): Results from a multi-dataset investigation. ISPRS Int. J. Geo-Inf., 7.10.3390/ijgi7090372 ↗
91. Lazzari, M., and Piccarreta, M. (2018). Landslide disasters triggered by extreme rainfall events: The case of Montescaglioso (Basilicata, Southern Italy). Geosciences, 8.10.3390/geosciences8100377 ↗
92. Shallow landslide susceptibility assessment in granitic rocks using GIS-based statistical methods: The contribution of the weathering grade map10.1007/s10346-018-0947-7 ↗
93. Activities of the Research Institute for Geo-Hydrological Protection, of the Italian National Research Council, World Centre of Excellence on landslide risk10.1007/s10346-019-01184-w ↗
94. Alvioli, M., Marchesini, I., Melelli, L., and Guth, P. (2020). Geomorphometry 2020, Conference Proceedings, Cnr Edizioni.
95. Canale, C., Barbaro, G., Foti, G., Petrucci, O., Besio, G., Fiamma, V., Barillà, G.C., Puntorieri, P., and Bruzzaniti, L. (2020, January 16–18). Floods and sea storms: Analysis of contemporaneity conditions in Calabria, Italy. Proceedings of the Eighth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020), Paphos, Cyprus.10.1117/12.2571215 ↗
96. Segoni, S., Rosi, A., Fanti, R., Gallucci, A., Monni, A., and Casagli, N. (2018). A regional-scale landslide warning system based on 20 years of operational experience. Water, 10.10.3390/w10101297 ↗
97. Lainas, S., Depountis, N., and Sabatakakis, N. (2021). Preliminary Forecasting of Rainfall-Induced Shallow Landslides in the Wildfire Burned Areas of Western Greece. Land, 10.10.3390/land10080877 ↗
98. The landslide of Pomarico, South Italy, occurred on 29 January 201910.1007/s10346-020-01455-x ↗
99. Application of the MoniFLaIR early warning system for rainfall-induced landslides in Piedmont region (Italy)10.1007/s10346-009-0189-9 ↗
100. Distributed optical fibre sensing for early detection of shallow landslides triggering10.1038/s41598-017-12610-1 ↗