Abstract
Landslide probability prediction plays an important role in understanding landslide information in advance and taking preventive measures. Many factors can influence the occurrence of landslides, which is easy to have a curse of dimensionality and thus lead to reduce prediction accuracy. Then the generalization ability of the model will also decline sharply when there are only small samples. To reduce the dimension of calculation and balance the model’s generalization and learning ability, this study proposed a landslide prediction method based on improved principal component analysis (PCA) and mixed kernel function least squares support vector regression (LSSVR) model. First, the traditional PCA was introduced with the idea of linear discrimination, and the dimensions of initial influencing factors were reduced from 8 to 3. The improved PCA can not only weight variables but also extract the original feature. Furthermore, combined with global and local kernel function, the mixed kernel function LSSVR model was framed to improve the generalization ability. Whale optimization algorithm (WOA) was used to optimize the parameters. Moreover, Root Mean Square Error (RMSE), the sum of squared errors (SSE), Mean Absolute Error (MAE), Mean Absolute Precentage Error (MAPE), and reliability were employed to verify the performance of the model. Compared with radial basis function (RBF) LSSVR model, Elman neural network model, and fuzzy decision model, the proposed method has a smaller deviation. Finally, the landslide warning level obtained from the landslide probability can also provide references for relevant decision-making departments in emergency response.
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References
Stanley TA, Kirschbaum DB, Sobieszczyk S, et al. (2020) Building a landslide hazard indicator with machine learning and land surface models. Environ Modell Softw 129: 104692. https://doi.org/10.1016/j.envsoft.2020.104692
Utomo D, Chen SH, Hsiung PA (2019) Landslide prediction with model switching. Appl Sci-Basel 9(9): 1939. https://doi.org/10.3390/app9091839
Zhang K, Wang S, Bao HJ, Zhao XM (2019) Characteristics and influencing factors of rainfall-induced landslide and debris flow hazards in Shaanxi Province, China,” Nat Hazard Earth Sys 19: 93–105. https://doi.org/10.5194/nhess-19-93-2019
Qiu H, Cui Y, Pei Y, et al. (2020) Temporal patterns of nonseismically triggered landslides in Shaanxi Province, China. Catena 187: 104356. https://doi.org/10.1016/j.catena.2019.104356
Bai DX, Tang JT, Lu GY, et al. (2020) The design and application of landslide monitoring and early warning system based on microservice architecture. Geomat Nat Haz Risk 11(1) 928–948. https://doi.org/10.1080/19475705.2020.1766580
Li SH, Wu LZ, Chen JJ (2020) Multiple data-driven approach for predicting landslide deformation. Landslides 17(3): 709–718. https://doi.org/10.1007/s10346-019-01320-6
Zhao NH, Hu B, Yan EC, et al. (2019) Research on the creep mechanism of Huangniba landslide in the Three Gorges Reservoir Area of China considering the seepage-stress coupling effect. B Eng Geol Environ 78(6): 4107–4121. https://doi.org/10.1007/s10064-018-1377-4
König T, Kux HJH, Mendes RM, et al. (2019) Shalstab mathematical model and WorldView-2 satellite images to identification of landslide-susceptible areas. Nat Hazards 97(3): 1127–1149. https://doi.org/10.1007/s11069-019-03691-4
Kim S, Kim M, An H, et al. (2019) Influence of subsurface flow by Lidar DEMs and physical soil strength considering a simple hydrologic concept for shallow landslide instability mapping. Catena 182:1–14. https://doi.org/10.1016/j.catena.2019.104137
Nsengiyumva JB, Luo G, Hakorimana E, et al. (2019) Comparative analysis of deterministic and semiquantitative approaches for shallow landslide risk modeling in Rwanda. Risk Anal 39(3):2576–2595. https://doi.org/10.1111/risa.13359
Sinarta IN, Rifa’i A, Fathani TF, et al. (2017) Slope stability assessment using trigger parameters and SINMAP Methods on Tamblingan-Buyan ancient mountain area in Buleleng Regency, Bali. Geosciences 7(4): 110. https://doi.org/10.3390/geosciences7040110
Hsu YC, Liu FK (2019) Combining TRIGRS and DEBRIS-2D Models for the simulation of a rainfall infiltration induced shallow landslide and subsequent debris flow. Water 11(5):890. https://doi.org/10.3390/w11050890
Marin RJ, María FV (2020) Influence of hydraulic properties on physically modelling slope stability and the definition of rainfall thresholds for shallow landslides. Geomorphology 351:(1–10). https://doi.org/10.1016/j.geomorph.2019.106976
Roslee R, Simon N, Tongkul F, et al. (2017) Landslide Susceptibility Analysis (LSA) using Deterministic Model (Infinite Slope) (DESSISM) in the Kota Kinabalu Area, Sabah, Malaysia. Geol Behav 1(1):6–9. https://doi.org/10.26480/gbr.01.2017.06.09
Johari A, Mousavi S (2019) An analytical probabilistic analysis of slopes based on limit equilibrium methods. B Eng Geol Environ 78(6):4333–4347. https://doi.org/10.1007/s10064-018-1408-1
Wang S, Zhang K, Van B, et al. (2020) Physically-based landslide prediction over a large region: Scaling low-resolution hydrological model results for high-resolution slope stability assessment. Environ Modell Softw 124: 1–14. https://doi.org/10.1016/j.envsoft.2019.104607
Gao CY, Cui XM (2016) Landslide risk assessment based on multiindex weighted grey target decision model. J Cent South Univ 47(2):524–530. https://doi.org/10.11817/j.issn.1672-7207.2016.02.024
Ba Q, Chen YM, Deng SS (2017) An improved information value model based on gray clustering for landslide susceptibility mapping. Isprs Int J Geo-Inf 6(1):18. https://doi.org/10.3390/ijgi6010018
Thi N, Liu CC (2019) A new approach using AHP to generate landslide susceptibility maps in the Chen-Yu-Lan Watershed, Taiwan. Sensors 19(3):505. https://doi.org/10.3390/s19030505
Guo WZ, Liu YK, Xu XZ, Li M (2015) Application of variable fuzzy pattern recognition method on landslide stability assessment. Transactions of the Csae 31(8):176–182. https://doi.org/10.3969/j.issn.1002-6819.2015.08.026
Cui W, He X, Yao M, et al. (2020) Landslide image captioning method based on Semantic Gate and Bi-Temporal LSTM. Isprs Int J Geo-Inf 9(4): 194. https://doi.org/10.3390/ijgi9040194
Li MH, Zhang L, Ding C, et al. (2020) Retrieval of historical surface displacements of the Baige landslide from time-series SAR observations for retrospective analysis of the collapse event. Remote Sens 240:111695. https://doi.org/10.1016/j.rse.2020.111695
Cho SE (2020) Failure distribution analysis of shallow landslides under rainfall infiltration based on fragility curves. Landslides 17(1):79–91. https://doi.org/10.1007/s10346-019-01257-w
Alexakis D, Agapiou A, Tzouvaras M, et al. (2014) Integrated use of GIS and remote sensing for monitoring landslides in transportation pavements: the case study of Paphos area in Cyprus. Nat Hazards:72(1):119–141. https://doi.org/10.1007/s11069-013-0770-3
Lu FM, Zeng H (2020) Application of Kalman Filter model in the landslide deformation forecast. Sci Rep 10(1):1028. https://doi.org/10.1038/s41598-020-57881-3
Emrehan KS, Ismail C, Taskin K (2020) A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping. Geocarto Int 35(4):341–363. https://doi.org/10.1080/10106049.2018.1516248
Pourghasemi HR, Kornejady A, Kerle N, Shabani F. (2020) Investigating the effects of different landslide positioning techniques, landslide partitioning approaches, and presence-absence balances on landslide susceptibility mapping. Catena 187:104364. https://doi.org/10.1016/j.catena.2019.104364
Guo Z, Chen L, Gui L, et al. (2020) Landslide displacement prediction based on variational mode decomposition and WA-GWO-BP model. Landslides 17(3):567–583. https://doi.org/10.1007/s10346-019-01314-4
Li Y, Sun R, Yin K, et al (2019) Forecasting of landslide displacements using a chaos theory based wavelet analysis-Volterra filter model. Sci Rep-UK 9:19853. https://doi.org/10.1038/s41598-019-56405-y
Merghadi A, Yunus AP, Dou J, et al. (2020) Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Sci Rev 207:103225. https://doi.org/10.1016/j.earscirev.2020.103225
Dou J, Yunus AP, et al. (2019) Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Sci Total Environ 662:332–346. https://doi.org/10.1016/j.scitotenv.2019.01.221
Sahin EK, Colkesen I (2019) Performance analysis of advanced decision tree-based ensemble learning algorithms for landslide susceptibility mapping. Geocarto Int. https://doi.org/10.1080/10106049.2019.1641560
Dou J, Yunusc AP, Merghadid A, et al. (2020) Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning. Sci Total Environ 720:137320.1–137320.16. https://doi.org/10.1016/j.scitotenv.2020.137320
Pham BT, Indra P (2019) A novel hybrid model of Bagging-based Naive Bayes Trees for landslide susceptibility assessment. B Eng Geol Environ 78(3):1911–1925. https://doi.org/10.1007/s10064-017-1202-5
He QF, Shahabi H, Shirzadi A, et al. (2019) Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms. Sci Total Environ 663: 1–15. https://doi.org/10.1016/j.scitotenv.2019.01.329
Li HJ, Xu Q, He YS, Deng J (2018) Prediction of landslide displacement with an ensemble-based extreme learning machine and copula models. Landslides. 15(1): 2047–2059. https://doi.org/10.1007/s10346-018-1020-2
Stanley TA, Kirschbaumc DB, Sobieszczyk S, et al. (2020) Building a landslide hazard indicator with machine learning and land surface models. Environ Modell Softw 129:104692. https://doi.org/10.1016/j.envsoft.2020.104692
Chang KT, Merghadi A, Yunus AP, et al. (2019) Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques. Sci Rep-UK 9:1603–16049. https://doi.org/10.1038/s41598-019-48773-2
Nhu VH, Shirzadi A, Shahabi H, et al. (2020) Shallow landslide susceptibility mapping by Random Forest Base Classifier and its Ensembles in a Semi-Arid Region of Iran. Forests 11(4):421. https://doi.org/10.3390/f11040421
Li HJ, Xu Q, He YS, et al (2020) Modeling and predicting reservoir landslide displacement with deep belief network and EWMA control charts: a case study in Three Gorges Reservoir. Landslides 17(3):693–707. https://doi.org/10.1007/s10346-019-01312-6
Huang FM, Huang JS, Zhou C, (2017) Landslide displacement prediction based on multivariate chaotic model and extreme learning machine. Eng Geol 218:173–186. https://doi.org/10.1016/j.enggeo.2017.01.016
Miao SX, Zhu Q, Zhang B, et al. (2017) Knowledge-guided consistent correlation analysis of multimode landslide monitoring data. Int J Geogr Inf Sci 31(11):2255–2271. https://doi.org/10.1080/13658816.2017.1356461
Yu LB, Cao Y, Zhou C, et al. (2019) Landslide susceptibility mapping combining information gain ratio and support vector machines: A case study from Wushan segment in the Three Gorges Reservoir Area, China. Applied Ences 9(22):4756. https://doi.org/10.3390/app9224756
Miao FS, Wu YP, Xie YH, Li YN (2018) Prediction of landslide displacement with step-like behavior based on multialgorithm optimization and a support vector regression model. Landslides 15(3):475–488. https://doi.org/10.1007/s10346-017-0883-y
Qiao SF, Feng CB, Yu PK, et al. (2020) Investigation on surface tilting in the failure process of shallow landslides. Sensors 20(9): 2662. https://doi.org/10.3390/s20092662
Kavoura K, Sabatakakis N (2020) Investigating landslide susceptibility procedures in Greece. Landslides 17(1):127–145. https://doi.org/10.1007/s10346-019-01271-y
Chinkulkijniwat A, Tirametatiparat T, Supotayan C, et al. (2019) Stability characteristics of shallow landslide triggered by rainfall. J Mt Sci 16(9): 2171–2183. https://doi.org/10.1007/s11629-019-5523-7
Valagussa A, Marc O, Frattini P, Crosta GB (2019) Seismic and geological controls on earthquake-induced landslide size. Earth Planet Sc Lett 506:268–281. https://doi.org/10.1016/j.epsl.2018.11.005
Hong HY, Chen W, Xu C, et al. (2017) Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy. Geocarto Int 32(2):139–154. https://doi.org/10.1080/10106049.2015.1130086
Wu RA, Zhang YS, Guo CB, et al. (2020) Landslide susceptibility assessment in mountainous area: a case study of Sichuan-Tibet railway, China. Environmental Earth ences 79(157): 4–16. https://doi.org/10.1007/s12665-020-8878-8
Kalantar B, Ueda N, Saeidi V, et al. (2020) Landslide susceptibility mapping: Machine and ensemble learning based on remote sensing big data. Remote Sens 12(11): 1737. https://doi.org/10.3390/rs12111737
Nhu VH, Shirzadi A, Shahabi H, et al. (2020) Shallow Landslide Susceptibility Mapping: A comparisons between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine algorithms. Iint J Env Res Pub He 17(8):2749. https://doi.org/10.3390/ijerph17082749
Gunadi DSA, Jaya INS, Tjahjono B (2017) Spatial modeling in landslide susceptibility. Idn J Electr Eng Co 5(1):139–146. https://doi.org/10.11591/ijeecs.v5.i1.pp139-146
Meier C, Jaboyedoff M, Derron M H, et al. (2020) A method to assess the probability of thickness and volume estimates of small and shallow initial landslide ruptures based on surface area. Landslides 3:976–982. https://doi.org/10.1007/s10346-020-01347-0
Guo JY, Wang X, Li Y (2017) Fault detection based on weighted difference principal component analysis. J Chemometr 5:2926. https://doi.org/10.1002/cem.2926
Kallio M, Guillaume JHA, Kummu M, et al. (2018) Spatial variation in seasonal water poverty index for Laos: An application of geographically weighted principal component analysis. Soc Indic Res 140:1131–1157. https://doi.org/10.1007/s11205-017-1819-6
Basu T, Das A, Pal S (2020) Application of geographically weighted principal component analysis and fuzzy approach for unsupervised landslide susceptibility mapping on Gish River Basin, India. Geocarto Int 2020:1–29. https://doi.org/10.1080/10106049.2020.1778105
Tian ZD, Li SJ (2017) A network traffic prediction method based on IFS algorithm optimised LSSVM. Int J Simul Model 9(4):200–213. https://doi.org/10.1504/IJESMS.2017.10008438
Mirjalili JS and Lewis A (2016) The Whale Optimization Algorithm. Adv Eng Softw 95: 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
Wang XF, Lu KS, Wang S, Huang XC (2017) Spatiotemporal assessment of rainstorm hazard risk in Qinling mountains of China. Hum Ecol Risk Assess 23(2):257–275. https://doi.org/10.1080/10807039.2016.1240609
Pham BT, Prakash I, Singh SK, et al. (2019) Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches. Catena 175:203–218. https://doi.org/10.1016/j.catena.2018.12.018
Zhuang JQ, Peng JB (2014) A coupled slope cutting-a prolonged rainfall-induced loess landslide: a 17 October 2011 case study. B Eng Geol Environ 73(4): 997–1011. https://doi.org/10.1007/s10064-014-0645-1
Liu HY, Li Y, Zhang Q, Hao PW, (2018) Deformation characteristic and mechanism of blisters in cement concrete bridge deck pavement. Constr Build Mater 172:358–369. https://doi.org/10.1016/j.conbuildmat.2018.03.234
Hong HY, Pradhan B, Xu C, Dieu TB (2015) Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena 133:266–281. https://doi.org/10.1016/j.catena.2015.05.019
Chen W, Xie XS, Wang JL, Pradhan B (2017) A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena 151:147–160. https://doi.org/10.1016/j.catena.2016.11.032
Acknowledgments
This work was supported by the Natural Science Foundation of Shaanxi Province (Grant No. 2019JQ206), in part by the Science and Technology Department of Shaanxi Province (Grant No.2020CGXNG-009), and in part by the Education Department of Shaanxi Province under Grant 17JK0346.
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Li, Lm., Cheng, Sk. & Wen, Zz. Landslide prediction based on improved principal component analysis and mixed kernel function least squares support vector regression model. J. Mt. Sci. 18, 2130–2142 (2021). https://doi.org/10.1007/s11629-020-6396-5
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DOI: https://doi.org/10.1007/s11629-020-6396-5