Landslide Susceptibility Modeling Using Integrated Ensemble Weights of Evidence with Logistic Regression and Random Forest Models
Source abstract
Abstract
The main aim of this study was to compare the performances of the hybrid approaches of traditional bivariate weights of evidence (WoE) with multivariate logistic regression (WoE-LR) and machine learning-based random forest (WoE-RF) for landslide susceptibility mapping. The performance of the three landslide models was validated with receiver operating characteristic (ROC) curves and area under the curve (AUC). The results showed that the areas under the curve obtained using the WoE, WoE-LR, and WoE-RF methods were 0.720, 0.773, and 0.802 for the training dataset, and were 0.695, 0.763, and 0.782 for the validation dataset, respectively. The results demonstrate the superiority of hybrid models and that the resultant maps would be useful for land use planning in landslide-prone areas.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Landslides and related hazards · Geotechnical Engineering and Analysis · Tree Root and Stability Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Degraded and Unused Land Consolidation Engineering, Ministry of Land and Resources of China, Xi’an 710075, China
Shaanxi Provincial Land Engineering Construction Group Co. Ltd., Xi’an 710075, China
College of Geology & Environment, Xi’an University of Science and Technology, Xi’an 710054, Shaanxi, China
Funding
the Opening fund of Key laboratory of Degraded and Unused Land Consolidation Engineering, the Ministry of Land and Resources · the National Natural Science Foundation of China · China Postdoctoral Science Foundation · Project funded by Shaanxi Province Postdoctoral Science Foundation
References (100+)
Show source-provided references
1. Effect of topography and soil parameterisation representing soil thicknesses on shallow landslide modelling10.1016/j.quaint.2015.03.057 ↗
2. The role of topography in the scaling distribution of landslide areas: A cellular automata modeling approach10.1016/j.geomorph.2017.04.017 ↗
3. A new appraisal of the ancona landslide based on geotechnical investigations and stability modelling10.1144/qjegh2013-028 ↗
4. Distribution and failure modes of the landslides in heitai terrace, china10.1016/j.enggeo.2017.09.016 ↗
5. The role of land use changes in the distribution of shallow landslides10.1016/j.scitotenv.2016.09.125 ↗
6. Characteristics of rainfall intensity, duration, and kinetic energy for landslide triggering in taiwan10.1016/j.enggeo.2017.10.006 ↗
7. Brief communication: Using averaged soil moisture estimates to improve the performances of a regional-scale landslide early warning system10.5194/nhess-18-807-2018 ↗
8. Water infiltration characteristics in loess associated with irrigation activities and its influence on the slope stability in heifangtai loess highland, china10.1016/j.enggeo.2017.12.020 ↗
9. Assessment of the effects of historical strong earthquakes on large-scale landslide groupings in the wei river midstream10.1016/j.enggeo.2018.01.020 ↗
10. Finite element simulation of an excavation-triggered landslide using large deformation theory10.1016/j.enggeo.2016.02.012 ↗
11. Implications of climate change on landslide hazard in central italy10.1016/j.scitotenv.2018.02.315 ↗
12. Modeling impacts of climate change on return period of landslide triggering10.1016/j.jhydrol.2018.10.036 ↗
13. Landslides in a changing climate10.1016/j.earscirev.2016.08.011 ↗
14. Guidelines for landslide susceptibility, hazard and risk zoning for land use planning10.1016/j.enggeo.2008.03.022 ↗
15. Recommendations for the quantitative analysis of landslide risk
16. Analysis and evaluation of landslide susceptibility: A review on articles published during 2005–2016 (periods of 2005–2012 and 2013–2016)10.1007/s12517-018-3531-5 ↗
17. Probabilistic landslide susceptibility mapping in the lai chau province of vietnam: Focus on the relationship between tectonic fractures and landslides10.1007/s00254-005-0019-x ↗
18. Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques10.1016/j.geomorph.2017.09.007 ↗
19. The 2010 yushu earthquake triggered landslide hazard mapping using gis and weight of evidence modeling10.1007/s12665-012-1624-0 ↗
20. A comparative study of landslide susceptibility mapping using weight of evidence, logistic regression and support vector machine and evaluated by sbas-insar monitoring: Zhouqu to wudu segment in bailong river basin, china10.1007/s12665-017-6640-7 ↗
21. Bivariate statistical index for landslide susceptibility mapping in the rorachu river basin of eastern sikkim himalaya, india10.1007/s41324-017-0156-9 ↗
22. Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in central nepal himalaya10.1007/s12517-012-0807-z ↗
23. Gis-based frequency ratio and index of entropy models for landslide susceptibility assessment in the caspian forest, northern iran10.1007/s13762-013-0464-0 ↗
24. Tien Bui, D., Shahabi, H., Shirzadi, A., Chapi, K., Alizadeh, M., Chen, W., Mohammadi, A., Ahmad, B., Panahi, M., and Hong, H. (2018). Landslide detection and susceptibility mapping by airsar data using support vector machine and index of entropy models in cameron highlands, malaysia. Remote Sens., 10.10.3390/rs10101527 ↗
25. Rainfall-induced landslide susceptibility assessment at the chongren area (china) using frequency ratio, certainty factor, and index of entropy
26. Gis-based landslide susceptibility mapping using analytical hierarchy process (ahp) and certainty factor (cf) models for the baozhong region of baoji city, china
27. Dou, J., Oguchi, T., Hayakawa, Y.S., Uchiyama, S., Saito, H., and Paudel, U. (2014). Gis-based landslide susceptibility mapping using a certainty factor model and its validation in the chuetsu area, central japan. Landslide Science for a Aafer Geoenvironment, Springer.10.1007/978-3-319-05050-8_65 ↗
28. Chen, W., Shahabi, H., Shirzadi, A., Hong, H., Akgun, A., Tian, Y., Liu, J., Zhu, A.-X., and Li, S. (2018). Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernel logistic regression classifier for landslide susceptibility modeling. Bull. Eng. Geol. Environ., 1–23.10.1007/s10064-018-1401-8 ↗
29. Spatial data analysis and application of evidential belief functions to shallow landslide susceptibility mapping at mt. Umyeon, seoul, korea10.1007/s10064-016-0919-x ↗
30. Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping
31. Zhang, T., Han, L., Chen, W., and Shahabi, H. (2018). Hybrid integration approach of entropy with logistic regression and support vector machine for landslide susceptibility modeling. Entropy, 20.10.3390/e20110884 ↗
32. Modeling and mapping landslide susceptibility zones using gis based multivariate binary logistic regression (lr) model in the rorachu river basin of eastern sikkim himalaya, india10.1007/s40808-018-0426-0 ↗
33. Performance evaluation of the gis-based data mining techniques of best-first decision tree, random forest, and naïve bayes tree for landslide susceptibility modeling10.1016/j.scitotenv.2018.06.389 ↗
34. Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at wadi tayyah basin, asir region, saudi arabia10.1007/s10346-015-0614-1 ↗
35. Landslide susceptibility modeling applying machine learning methods: A case study from longju in the three gorges reservoir area, china10.1016/j.cageo.2017.11.019 ↗
36. Pham, B.T., Jaafari, A., Prakash, I., and Bui, D.T. (2018). A novel hybrid intelligent model of support vector machines and the multiboost ensemble for landslide susceptibility modeling. Bull. Eng. Geol. Environ., 1–22.10.1007/s10064-018-1281-y ↗
37. Landslide susceptibility mapping using j48 decision tree with adaboost, bagging and rotation forest ensembles in the guangchang area (china)10.1016/j.catena.2018.01.005 ↗
38. A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment10.1080/10106049.2018.1425738 ↗
39. Chen, W., Shahabi, H., Zhang, S., Khosravi, K., Shirzadi, A., Chapi, K., Pham, B.T., Zhang, T., Zhang, L., and Chai, H. (2018). Landslide susceptibility modeling based on gis and novel bagging-based kernel logistic regression. Appl. Sci., 8.10.3390/app8122540 ↗
40. Truong, X., Mitamura, M., Kono, Y., Raghavan, V., Yonezawa, G., Truong, X., Do, T., Tien Bui, D., and Lee, S. (2018). Enhancing prediction performance of landslide susceptibility model using hybrid machine learning approach of bagging ensemble and logistic model tree. Appl. Sci., 8.10.3390/app8071046 ↗
41. Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at himalayan area (india) using gis10.1016/j.catena.2016.09.007 ↗
42. Pham, B.T., Shirzadi, A., Tien Bui, D., Prakash, I., and Dholakia, M.B. (2017). A hybrid machine learning ensemble approach based on a radial basis function neural network and rotation forest for landslide susceptibility modeling: A case study in the himalayan area, india. Int. J. Sediment. Res.10.1016/j.ijsrc.2017.09.008 ↗
43. Landslide spatial modeling: Introducing new ensembles of ann, maxent, and svm machine learning techniques10.1016/j.geoderma.2017.06.020 ↗
44. Spatial prediction of landslides using a hybrid machine learning approach based on random subspace and classification and regression trees10.1016/j.geomorph.2017.12.008 ↗
45. Gis-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in iran10.1007/s12665-016-5424-9 ↗
46. A tool for classification and regression using random forest methodology: Applications to landslide susceptibility mapping and soil thickness modeling10.1007/s10666-016-9538-y ↗
47. Bagging predictors10.1007/BF00058655 ↗
48. Gis-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with adaboost, bagging, and multiboost ensemble frameworks10.1007/s12665-016-5919-4 ↗
49. Multiboost with enn-based ensemble fault diagnosis method and its application in complicated chemical process10.1007/s11771-016-0368-5 ↗
50. A hybrid machine learning ensemble approach based on a radial basis function neural network and rotation forest for landslide susceptibility modeling: A case study in the himalayan area, india10.1016/j.ijsrc.2017.09.008 ↗
51. A hybrid model using machine learning methods and gis for potential rockfall source identification from airborne laser scanning data10.1007/s10346-018-0990-4 ↗
52. Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility10.1016/j.catena.2018.08.025 ↗
53. A comparative assessment of gis-based data mining models and a novel ensemble model in groundwater well potential mapping10.1016/j.jhydrol.2017.03.020 ↗
54. Gis-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models10.1016/j.scitotenv.2018.04.055 ↗
55. Kadavi, P., Lee, C.-W., and Lee, S. (2018). Application of ensemble-based machine learning models to landslide susceptibility mapping. Remote Sens., 10.10.3390/rs10081252 ↗
56. Gis-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method10.1016/j.catena.2018.01.012 ↗
57. A novel hybrid bivariate statistical method entitled froc for landslide susceptibility assessment10.1007/s12665-018-7852-1 ↗
58. Rapid landslides: The importance of understanding mechanisms and rupture surface mechanics10.1144/1470-9236/06-030 ↗
59. The new landslide inventory of tuscany (italy) updated with ps-insar: Geomorphological features and landslide distribution10.1007/s10346-017-0861-4 ↗
60. The varnes classification of landslide types, an update10.1007/s10346-013-0436-y ↗
61. Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree10.1007/s10346-015-0557-6 ↗
62. Assessment of landslide susceptibility on the natural terrain of lantau island, hong kong10.1007/s002540000163 ↗
63. Landslide susceptibility mapping for a part of tectonic kelkit valley (eastern black sea region of turkey)10.1016/j.geomorph.2006.10.036 ↗
64. Application and comparison of decision tree-based machine learning methods in landside susceptibility assessment at pauri garhwal area, uttarakhand, india10.1007/s40710-017-0248-5 ↗
65. Application of analytic hierarchy process model for landslide susceptibility mapping in the gangu county, gansu province, china10.1007/s12665-015-5194-9 ↗
66. Susceptibility assessment of shallow landslides in hulu kelang area, kuala lumpur, malaysia using analytical hierarchy process and frequency ratio10.1007/s10706-014-9818-8 ↗
67. Comparison of gis-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of ambon, indonesia10.1016/j.geomorph.2018.06.006 ↗
68. A data-driven approach to landslide-susceptibility mapping in mountainous terrain: Case study from the northwest himalayas, pakistan10.1061/(ASCE)NH.1527-6996.0000302 ↗
69. Application of frequency ratio, statistical index, and index of entropy models and their comparison in landslide susceptibility mapping for the baozhong region of baoji, china10.1007/s12517-014-1554-0 ↗
70. Digital terrain modelling: A review of hydrological, geomorphological, and biological applications10.1002/hyp.3360050103 ↗
71. Landslide susceptibility modelling using gis-based machine learning techniques for chongren county, jiangxi province, china10.1016/j.scitotenv.2018.01.124 ↗
72. A physically based, variable contributing area model of basin hydrology/un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant10.1080/02626667909491834 ↗
73. A comparison of five methods in landslide susceptibility assessment: A case study from the 330-kv transmission line in gansu region, china10.1007/s12665-018-7814-7 ↗
74. A comparative study on the landslide susceptibility mapping using logistic regression and statistical index models10.1007/s12517-017-2961-9 ↗
75. Gis-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in ardesen (turkey): Comparisons of results and confirmations10.1016/j.catena.2007.01.003 ↗
76. A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: A case study at İzmir, turkey10.1007/s10346-011-0283-7 ↗
77. Chen, W., Yan, X., Zhao, Z., Hong, H., Bui, D.T., and Pradhan, B. (2018). Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive bayes and rbfnetwork models for the long county area (china). Bull. Eng. Geol. Environ., 1–20.10.1007/s10064-018-1256-z ↗
78. Gis-based support vector machine modeling of earthquake-triggered landslide susceptibility in the jianjiang river watershed, China10.1016/j.geomorph.2011.12.040 ↗
79. Using multiple logistic regression and gis technology to predict landslide hazard in northeast kansas, USA10.1016/S0013-7952(03)00069-3 ↗
80. A comparison between index of entropy and catastrophe theory methods for mapping groundwater potential in an arid region10.1007/s10661-015-4801-2 ↗
81. Application of a physically based model to forecast shallow landslides at a regional scale10.5194/nhess-18-1919-2018 ↗
82. Three-dimensional geological structures and sliding factors and modes of loess landslides10.1007/s12665-018-7863-y ↗
83. Flow-type landslides in pyroclastic soils on flysch bedrock in southern italy: The bosco de’ preti case study10.1007/s10346-017-0854-3 ↗
84. Landslide occurrence as a response to land use change: A review of evidence from new zealand10.1016/S0341-8162(02)00170-4 ↗
85. Centrifuge model test of an irrigation-induced loess landslide in the heifangtai loess platform, northwest china10.1007/s11629-017-4490-0 ↗
86. Analysis of the phenology of global vegetation using meteorological satellite data10.1080/01431168508948281 ↗
87. A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility10.1016/j.catena.2016.11.032 ↗
88. Lithological and seasonal control on rainfall thresholds for the possible initiation of landslides in central italy10.1016/j.geomorph.2011.10.005 ↗
89. Runoff process on a steep forested slope10.1016/0022-1694(88)90096-0 ↗
90. Systematic approach to dealing with uncertainty of geoscience information in mineral exploration
91. Gis-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping10.1007/s00254-007-0818-3 ↗
92. Generalized linear models10.2307/2532465 ↗
93. A hybrid model using logistic regression and wavelet transformation to detect traffic incidents10.1016/j.iatssr.2016.06.001 ↗
94. Semantic segmentation of images exploiting dct based features and random forest10.1016/j.patcog.2015.10.021 ↗
95. Congestive heart failure detection using random forest classifier10.1016/j.cmpb.2016.03.020 ↗
96. Random forests10.1023/A:1010933404324 ↗
97. Landslide susceptibility mapping using bayesian approach in the sultan mountains (akşehir, turkey)10.1007/s11069-011-9853-1 ↗
98. Gis-based landslide susceptibility modelling: A comparative assessment of kernel logistic regression, naïve-bayes tree, and alternating decision tree models10.1080/19475705.2017.1289250 ↗
99. Assessment of susceptibility to rainfall-induced landslides using improved self-organizing linear output map, support vector machine, and logistic regression10.1016/j.enggeo.2017.05.009 ↗
100. Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods10.1016/j.geomorph.2017.12.007 ↗