Abstract
Landslides are a common natural disaster, having severe socio-economic effects and posing immense threat to safety, such as loss of life at a global scale. Modeling and predicting the possibility of landslides are important in order to monitor and prevent their negative consequences. In this study, landslides are the primary research object. Further, the frequency ratio (FR) method was applied to the random forest (RF), support vector machine (SVM), and decision tree (DT) regression algorithms for landslide sensitivity assessment. It was also applied to landslide risk assessment mapping in the Longmen Mountain area. Therefore, taking into account the positive and negative sample balance, 7774 historical landslide points and 7774 non-landslide points were selected and divided them into training sets and test sets. The influence factors were selected and analyzed through multicollinearity analysis and the FR method. To improve the performance of the model and the accuracy of the findings, the individual environmental factors are normalized. Subsequently, the LSI (landslide susceptibility index), was obtained by calculating the frequency ratio. Following this, the RF, SVM, and DT were used to construct the model. The trained model calculates the landslide probability of each cell in the study area and generates the resultant susceptibility map. The receiver operating characteristic (ROC) curve and R2 of this region were calculated to evaluate the model’s performance. The results indicate that RF obtained the highest predictive performance (area under the curve (AUC) = 0.82) in landslide risk prediction, followed by SVM (AUC = 0.8) and DT (AUC = 0.69). The results of this study serve as a predictive map for landslide susceptibility areas and provide critical support for the security of lives and property for the human and socio-economic development in the Longmen Mountain region. In addition, the experiment results reveal that the machine learning model based on the FR method can improve the accuracy and performance of methods in studies related to landslide susceptibility. The method is equally applicable to research in other fields.
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Data availability
The data sets used and analyzed during the current study are available from the corresponding author on reasonable request.
References
Abu El-Magd SA, Ali SA, Pham QB (2021) Spatial modeling and susceptibility zonation of landslides using random forest, naïve bayes and K-nearest neighbor in a complicated terrain. Earth Sci Inf 14:1227–1243. https://doi.org/10.1007/s12145-021-00653-y
Akgun A, Dag S, Bulut F (2008) Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ Geol 54:1127–1143. https://doi.org/10.1007/s00254-007-0882-8
Ali MZ, Chu H-J, Chen Y-C, Ullah S (2021) Machine learning in earthquake- and typhoon-triggered landslide susceptibility mapping and critical factor identification. Environ Earth Sci 80:233. https://doi.org/10.1007/s12665-021-09510-z
Arnone E, Francipane A, Scarbaci A et al (2016) Effect of raster resolution and polygon-conversion algorithm on landslide susceptibility mapping. Environ Model Softw 84:467–481. https://doi.org/10.1016/j.envsoft.2016.07.016
Chen W, Yan X, Zhao Z et al (2019) 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 78:247–266. https://doi.org/10.1007/s10064-018-1256-z
Chen X, Liu C, Wang M, Zhou Q (2018) Causes of unusual distribution of coseismic landslides triggered by the Mw 6.1 2014 Ludian, Yunnan, China earthquake. J Asian Earth Sci 159:17–23. https://doi.org/10.1016/j.jseaes.2018.03.010
Choi J, Oh H-J, Lee H-J et al (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124:12–23. https://doi.org/10.1016/j.enggeo.2011.09.011
Chu L, Wang L-J, Jiang J et al (2019) Comparison of landslide susceptibility maps using random forest and multivariate adaptive regression spline models in combination with catchment map units. Geosci J 23:341–355. https://doi.org/10.1007/s12303-018-0038-8
Crozier MJ (2017) A proposed cell model for multiple-occurrence regional landslide events: implications for landslide susceptibility mapping. Geomorphology 295:480–488. https://doi.org/10.1016/j.geomorph.2017.07.032
Feizizadeh B, Shadman Roodposhti M, Jankowski P, Blaschke T (2014) A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Comput Geosci 73:208–221. https://doi.org/10.1016/j.cageo.2014.08.001
Galli M, Ardizzone F, Cardinali M et al (2008) Comparing landslide inventory maps. Geomorphology 94:268–289. https://doi.org/10.1016/j.geomorph.2006.09.023
Hassangavyar MB, Damaneh HE, Pham QB et al (2022) Evaluation of re-sampling methods on performance of machine learning models to predict landslide susceptibility. Geocarto Int 37:2772–2794. https://doi.org/10.1080/10106049.2020.1837257
He Q, Wang M, Liu K (2021) Rapidly assessing earthquake-induced landslide susceptibility on a global scale using random forest. Geomorphology 391:107889. https://doi.org/10.1016/j.geomorph.2021.107889
Hong H, Miao Y, Liu J, Zhu A-X (2019) Exploring the effects of the design and quantity of absence data on the performance of random forest-based landslide susceptibility mapping. CATENA 176:45–64. https://doi.org/10.1016/j.catena.2018.12.035
Huang P, Peng L, Pan H (2020) Linking the Random forests model and GIS to assess geo-hazards risk: a case study in Shifang county, China. IEEE Access 8:28033–28042. https://doi.org/10.1109/ACCESS.2020.2972005
Kalantar B, Pradhan B, Naghibi SA et al (2018) Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomat Nat Haz Risk 9:49–69. https://doi.org/10.1080/19475705.2017.1407368
Khan H, Shafique M, Khan MA et al (2019) Landslide susceptibility assessment using frequency ratio, a case study of northern Pakistan. Egypt J Remote Sens Space Sci 22:11–24. https://doi.org/10.1016/j.ejrs.2018.03.004
Kim J-C, Lee S, Jung H-S, Lee S (2018) Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea. Geocarto Int 33:1000–1015. https://doi.org/10.1080/10106049.2017.1323964
Lee M-J, Park I, Lee S (2015) Forecasting and validation of landslide susceptibility using an integration of frequency ratio and neuro-fuzzy models: a case study of Seorak mountain area in Korea. Environ Earth Sci 74:413–429. https://doi.org/10.1007/s12665-015-4048-9
Li H, Chen Y, Deng S et al (2019) Eigenvector spatial filtering-based logistic regression for landslide susceptibility assessment. IJGI 8:332. https://doi.org/10.3390/ijgi8080332
Li L, Lan H, Guo C et al (2017) A modified frequency ratio method for landslide susceptibility assessment. Landslides 14:727–741. https://doi.org/10.1007/s10346-016-0771-x
Li N, Tang C, Yang T, Chen M (2020) Analysing post-earthquake landslide susceptibility using multi-temporal landslide inventories — a case study in Miansi Town of China. J Mt Sci 17:358–372. https://doi.org/10.1007/s11629-019-5681-7
Li X, Chen X, Yang T et al (2022) Projection of extreme precipitation in the Minjiang River Basin, Southeast China. J Water Clim Change 13:4177–4193. https://doi.org/10.2166/wcc.2022.145
Lin Z, Sun X, Ji Y (2022) Landslide displacement prediction model using time series analysis method and modified LSTM model. Electronics 11:1519. https://doi.org/10.3390/electronics11101519
Mandal SP, Chakrabarty A, Maity P (2018) Comparative evaluation of information value and frequency ratio in landslide susceptibility analysis along national highways of Sikkim Himalaya. Spat Inf Res 26:127–141. https://doi.org/10.1007/s41324-017-0160-0
Pisano L, Zumpano V, Malek Ž et al (2017) Variations in the susceptibility to landslides, as a consequence of land cover changes: a look to the past, and another towards the future. Sci Total Environ 601–602:1147–1159. https://doi.org/10.1016/j.scitotenv.2017.05.231
Polykretis C, Chalkias C (2018) Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models. Nat Hazards 93:249–274. https://doi.org/10.1007/s11069-018-3299-7
Pradhan AMS, Kim YT (2016) Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping. CATENA 140:125–139. https://doi.org/10.1016/j.catena.2016.01.022
Radjah VYG, Suryatmojo H, Ngadisih (2020) Landslide susceptibility zone using frequency ratio method in Karangkobar catchment, Merawu watershed, Banjarnegara district, Central Java province. IOP Conf Ser: Earth Environ Sci 451:012087. https://doi.org/10.1088/1755-1315/451/1/012087
Reichenbach P, Rossi M, Malamud BD et al (2018) A review of statistically-based landslide susceptibility models. Earth Sci Rev 180:60–91. https://doi.org/10.1016/j.earscirev.2018.03.001
Shu H, Guo Z, Qi S et al (2021) Integrating landslide typology with weighted frequency ratio model for landslide susceptibility mapping: a case study from Lanzhou City of Northwestern China. Remote Sens 13:3623. https://doi.org/10.3390/rs13183623
Song Y, Fan W, Yu N et al (2022) Rainfall induced shallow landslide temporal probability modelling and early warning research in mountains areas: a case study of Qin-Ba Mountains, Western China. Remote Sens 14:5952. https://doi.org/10.3390/rs14235952
Tian Y, Xu C, Hong H et al (2019a) Mapping earthquake-triggered landslide susceptibility by use of artificial neural network (ANN) models: an example of the 2013 Minxian (China) Mw 5.9 event. Geomat Nat Haz Risk 10:1–25. https://doi.org/10.1080/19475705.2018.1487471
Tian Y, Xu C, Ma S et al (2019b) Inventory and spatial distribution of landslides triggered by the 8th August 2017 MW 6.5 Jiuzhaigou Earthquake, China. J Earth Sci 30:206–217. https://doi.org/10.1007/s12583-018-0869-2
Tsangaratos P, Ilia I (2016) Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: the influence of models complexity and training dataset size. CATENA 145:164–179. https://doi.org/10.1016/j.catena.2016.06.004
Wei K, Ouyang C, Duan H et al (2020) Reflections on the catastrophic 2020 Yangtze River Basin flooding in southern China. The Innovation 1:100038. https://doi.org/10.1016/j.xinn.2020.100038
Xue Y, Kong F, Li S et al (2021) China starts the world’s hardest “sky-high road” project: challenges and countermeasures for Sichuan-Tibet railway. The Innovation 2:100105. https://doi.org/10.1016/j.xinn.2021.100105
Yang X, Liu R, Yang M et al (2021) Incorporating landslide spatial information and correlated features among conditioning factors for landslide susceptibility mapping. Remote Sens 13:2166. https://doi.org/10.3390/rs13112166
Yang Z, Lan H, Gao X et al (2015) Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China. Nat Hazards 75:2467–2487. https://doi.org/10.1007/s11069-014-1441-8
Zhao D, Jiao Y, Wang J et al (2020a) Comparative performance assessment of landslide susceptibility models with presence-only, presence-absence, and pseudo-absence data. J Mt Sci 17:2961–2981. https://doi.org/10.1007/s11629-020-6277-y
Zhao L, Wu X, Niu R et al (2020b) Using the rotation and random forest models of ensemble learning to predict landslide susceptibility. Geomat Nat Haz Risk 11:1542–1564. https://doi.org/10.1080/19475705.2020.1803421
Zhao S, Zhao Z (2021) A comparative study of landslide susceptibility mapping using SVM and PSO-SVM models based on grid and slope units. Math Probl Eng 2021:1–15. https://doi.org/10.1155/2021/8854606
Zhu A-X, Miao Y, Wang R et al (2018a) A comparative study of an expert knowledge-based model and two data-driven models for landslide susceptibility mapping. CATENA 166:317–327. https://doi.org/10.1016/j.catena.2018.04.003
Zhu A-X, Miao Y, Yang L et al (2018b) Comparison of the presence-only method and presence-absence method in landslide susceptibility mapping. CATENA 171:222–233. https://doi.org/10.1016/j.catena.2018.07.012
Zhu Q, Chen L, Hu H et al (2020) Unsupervised feature learning to improve transferability of landslide susceptibility representations. IEEE J Sel Top Appl Earth Observations Remote Sensing 13:3917–3930. https://doi.org/10.1109/JSTARS.2020.3006192
Funding
This work was supported by the National Natural Science Foundation of China (No. 42071222), the Sichuan Science and Technology Program (No. 2022JDJQ0015), and the Tianfu Qingcheng Program (No. ZX20220027).
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ziyan Huang and Peng Li. The first draft of the manuscript was written by Ziyan Huang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Huang, ., Peng, L., Li, S. et al. GIS-based landslide susceptibility mapping in the Longmen Mountain area (China) using three different machine learning algorithms and their comparison. Environ Sci Pollut Res 30, 88612–88626 (2023). https://doi.org/10.1007/s11356-023-28730-3
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DOI: https://doi.org/10.1007/s11356-023-28730-3