Skip to main content

Advertisement

SpringerLink
Log in

Landslide susceptibility prediction using an incremental learning Bayesian Network model considering the continuously updated landslide inventories

  • Original Paper
  • Published:
Bulletin of Engineering Geology and the Environment Aims and scope Submit manuscript

Abstract

Existing studies relating to landslide susceptibility prediction (LSP) either do not pay enough attentions to the continuously updated landslide inventories or use batch learning methods for LSP, resulting in the insufficient use of the entire landslide inventory. To overcome this problem, the Incremental Learning theory combined with a Bayesian Network (ILBN) model is constructed for LSP. Wencheng County of China is taken as the study area, a landslide inventory from 1985 to 2019 and 10 conditioning factors are mapped and analyzed. Then, the LSP results of the ILBN model are compared with the batch learning-based multilayer perceptron (BL-MLP) and support vector machine (BL-SVM) models. Results show that the LSP accuracies of ILBN_0 (ILBN modeling of initial landslide inventory), ILBN_1 (the first Incremental Learning model), and ILBN_2 (the second Incremental Learning model) increase gradually with the AUC value of 0.807, 0.813, and 0.835, respectively. The LSM produced by the ILBN model is more consistent with the law of landslides distribution in the study area. The mean values of ILBN_0, ILBN_1, and ILBN_2 are 0.307, 0.287, and 0.245, and the standard deviations are 0.278, 0.281, and 0.308, respectively. Meanwhile, the characteristics of LSIs in Wencheng County are in line with the actual landslides distribution with the main controlling factors of lithology, elevation, and normalized difference building indexes determined by the weighted mean method. Furthermore, the LSP results of ILBN model are superior to those of the BL-MLP and BL-SVM models. It is concluded that the ILBN model can better address the long-term, continuous LSP using the new added landslide inventory.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Abuzied SM, Alrefaee HA (2018) Spatial prediction of landslide-susceptible zones in El-qaá area, Egypt, using an integrated approach based on GIS statistical analysis. Bull Eng Geol Env 78(4):2169–2195. https://doi.org/10.1007/s10064-018-1302-x

    Article  Google Scholar 

  • Aghdam IN, Varzandeh MHM, Pradhan B (2016) Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran). Environ Earth Sci 75(7):553. https://doi.org/10.1007/s12665-015-5233-6

    Article  Google Scholar 

  • Ali SA, Parvin F, Vojteková J et al (2021) Gis-based landslide susceptibility modeling: a comparison between fuzzy multi-criteria and machine learning algorithms. Geosci Front 12(2):857–876. https://doi.org/10.1016/j.gsf.2020.09.004

    Article  Google Scholar 

  • Armaş I (2011) Weights of evidence method for landslide susceptibility mapping. Prahova Subcarpathians, Romania. Nat Hazards 60(3):937–950. https://doi.org/10.1007/s11069-011-9879-4

    Article  Google Scholar 

  • Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, japan. Landslides 1(1):73–81

    Article  Google Scholar 

  • Bai B, Nie Q, Zhang Y et al (2020) Cotransport of heavy metals and SiO2 particles at different temperatures by seepage. J Hydrol. https://doi.org/10.1016/j.jhydrol.2020.125771

    Article  Google Scholar 

  • Bai B, Rao D, Chang T et al (2019) A nonlinear attachment-detachment model with adsorption hysteresis for suspension-colloidal transport in porous media. J Hydrol 578:124080

    Article  Google Scholar 

  • Bourenane H, Meziani AA, Benamar DA (2021) Application of gis-based statistical modeling for landslide susceptibility mapping in the city of Azazga, Northern Algeria. Bull Eng Geol Env 80(10):7333–7359. https://doi.org/10.1007/s10064-021-02386-0

    Article  Google Scholar 

  • Carpenter GA, Grossberg S, Rosen DB (1991) Fuzzy art: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Netw 4(6):759–771. https://doi.org/10.1016/0893-6080(91)90056-B

    Article  Google Scholar 

  • Chakraborty A, Goswami D (2017) Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN). Arab J Geosci 10(17):385. https://doi.org/10.1007/s12517-017-3167-x

    Article  Google Scholar 

  • Chang Z, Du Z, Zhang F et al (2020) Landslide susceptibility prediction based on remote sensing images and GIS: comparisons of supervised and unsupervised machine learning models. Remote Sens 12(3):502

    Article  Google Scholar 

  • Chen W, Peng J, Hong H et al (2018a) Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Sci Total Environ 626:1121–1135. https://doi.org/10.1016/j.scitotenv.2018.01.124

    Article  Google Scholar 

  • Chen W, Pourghasemi HR, Panahi M et al (2017) Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques. Geomorphology 297:69–85. https://doi.org/10.1016/j.geomorph.2017.09.007

    Article  Google Scholar 

  • Chen W, Shahabi H, Shirzadi A et al (2018b) Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernel logistic regression classifier for landslide susceptibility modeling. Bull Eng Geol Env 78(6):4397–4419. https://doi.org/10.1007/s10064-018-1401-8

    Article  Google Scholar 

  • Chen W, Yan X, Zhao Z et al (2018c) 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 Env 78(1):247–266. https://doi.org/10.1007/s10064-018-1256-z

    Article  Google Scholar 

  • Dang V-H, Dieu TB, Tran X-L et al (2018) Enhancing the accuracy of rainfall-induced landslide prediction along mountain roads with a GIS-based random forest classifier. Bull Eng Geol Env 78(4):2835–2849. https://doi.org/10.1007/s10064-018-1273-y

    Article  Google Scholar 

  • Depountis N, Nikolakopoulos K, Kavoura K et al (2019) Description of a GIS-based rockfall hazard assessment methodology and its application in mountainous sites. Bull Eng Geol Env 79(2):645–658. https://doi.org/10.1007/s10064-019-01590-3

    Article  Google Scholar 

  • Diehl CP, Cauwenberghs G (2003) SVM incremental learning, adaptation and optimization. In: Proceedings of the International Joint Conference on Neural Networks

  • Ding Y-N, Li D-Q, Zarei C et al (2021) Probabilistically quantifying the effect of geotechnical anisotropy on landslide susceptibility. Bull Eng Geol Env 80(8):6615–6627. https://doi.org/10.1007/s10064-021-02197-3

    Article  Google Scholar 

  • Emami SN, Yousefi S, Pourghasemi HR et al (2020) A comparative study on machine learning modeling for mass movement susceptibility mapping (a case study of Iran). Bull Eng Geol Env 79(10):5291–5308. https://doi.org/10.1007/s10064-020-01915-7

    Article  Google Scholar 

  • Ferrer-Troyano F, Aguilar-Ruiz JS, Riquelme JC (2005) Incremental rule learning based on example nearness from numerical data streams. In; Proceedings of the 2005 ACM symposium on Applied computing, Santa Fe, New Mexico. https://doi.org/10.1145/1066677.1066808

  • Galli M, Ardizzone F, Cardinali M et al (2008) Comparing landslide inventory maps. Geomorphology 94(3):268–289

    Article  Google Scholar 

  • Gnyawali KR, Zhang Y, Wang G et al (2019) Mapping the susceptibility of rainfall and earthquake triggered landslides along China-Nepal highways. Bull Eng Geol Env 79(2):587–601. https://doi.org/10.1007/s10064-019-01583-2

    Article  Google Scholar 

  • Gu B, Sheng VS, Wang Z et al (2015) Incremental learning for ν-support vector regression. Neural Netw 67:140–150

    Article  Google Scholar 

  • Guzzetti F, Mondini AC, Cardinali M et al (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112(1):42–66. https://doi.org/10.1016/j.earscirev.2012.02.001

    Article  Google Scholar 

  • Hearn GJ, Hart AB (2019) Landslide susceptibility mapping: a practitioner’s view. Bull Eng Geol Env 78(8):5811–5826. https://doi.org/10.1007/s10064-019-01506-1

    Article  Google Scholar 

  • Herrmann D, Wendolsky R, Federrath H (2009) Website fingerprinting: attacking popular privacy enhancing technologies with the multinomial naïve-Bayes classifier. In: Proceedings of the 2009 ACM workshop on Cloud computing security

  • Hong H, Pourghasemi HR, Pourtaghi ZS (2016) Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology 259:105–118

    Article  Google Scholar 

  • Hosseini S, Ivanov D (2019) A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach. Ann Oper Res 1–27

  • Huang F, Cao Z, Guo J et al (2020a) Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. CATENA 191:104580

    Article  Google Scholar 

  • Huang F, Cao Z, Jiang S-H et al (2020b) Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model. Landslides 17(12):2919–2930. https://doi.org/10.1007/s10346-020-01473-9

    Article  Google Scholar 

  • Huang F, Chen J, Du Z et al (2020c) Landslide susceptibility prediction considering regional soil erosion based on machine-learning models. ISPRS Int J Geo Inf 9(6):377. https://doi.org/10.3390/ijgi9060377

  • Huang F, Chen J, Yao C et al (2020d) SUSLE: a slope and seasonal rainfall-based RUSLE model for regional quantitative prediction of soil erosion. Bull Eng Geol Env 79(10):5213–5228. https://doi.org/10.1007/s10064-020-01886-9

    Article  Google Scholar 

  • Huang F, Yao C, Liu W et al (2018) Landslide susceptibility assessment in the Nantian area of China: a comparison of frequency ratio model and support vector machine. Geomat Nat Haz Risk 9(1):919–938. https://doi.org/10.1080/19475705.2018.1482963

    Article  Google Scholar 

  • Huang F, Ye Z, Jiang S-H et al (2021) Uncertainty study of landslide susceptibility prediction considering the different attribute interval numbers of environmental factors and different data-based models. CATENA 202:105250. https://doi.org/10.1016/j.catena.2021.105250

    Article  Google Scholar 

  • Huang F, Yin K, Huang J et al (2017) Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine. Eng Geol 223:11–22

    Article  Google Scholar 

  • Hungr O, Evans SG, Bovis MJ et al (2001) A review of the classification of landslides of the flow type. Environ Eng Geosci 7(3):221–238

    Article  Google Scholar 

  • Huang Y, Zhao L (2018) Review on landslide susceptibility mapping using support vector machines. CATENA 165:520–529

    Article  Google Scholar 

  • Kavzoglu T, Kutlug Sahin E, Colkesen I (2015) Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm. Eng Geol 192:101–112. https://doi.org/10.1016/j.enggeo.2015.04.004

    Article  Google Scholar 

  • Karakas G, Kocaman S, Gokceoglu C (2022) Comprehensive performance assessment of landslide susceptibility mapping with MLP and random forest: a case study after Elazig earthquake (24 Jan 2020, Mw 6.8), Turkey. Environ Earth Sci 81:144

    Article  Google Scholar 

  • Khosravi K, Shahabi H, Pham BT et al (2019) A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. J Hydrol 573:311–323. https://doi.org/10.1016/j.jhydrol.2019.03.073

    Article  Google Scholar 

  • Lamirel J-C, Boulila Z, Ghribi M et al (2010) A new incremental growing neural gas algorithm based on clusters labeling maximization: application to clustering of heterogeneous textual data. Berlin, Heidelberg

  • Li D, Huang F, Yan L et al (2019) Landslide susceptibility prediction using particle-swarm-optimized multilayer perceptron: comparisons with multilayer-perceptron-only, BP neural network, and information value models. Appl Sci 9(18):3664

    Article  Google Scholar 

  • Li L, Lan H, Guo C et al (2016) A modified frequency ratio method for landslide susceptibility assessment. Landslides 14(2):727–741. https://doi.org/10.1007/s10346-016-0771-x

    Article  Google Scholar 

  • Li S, Xu Q, Tang M et al (2018) Characterizing the spatial distribution and fundamental controls of landslides in the Three Gorges Reservoir Area, China. Bull Eng Geol Env 78(6):4275–4290. https://doi.org/10.1007/s10064-018-1404-5

    Article  Google Scholar 

  • Li W, Fan X, Huang F et al (2020) Uncertainties analysis of collapse susceptibility prediction based on remote sensing and GIS: influences of different data-based models and connections between collapses and environmental factors. Remote Sens 12(24):4134. https://doi.org/10.3390/rs12244134

  • Lin Y, Vosselman G, Cao Y et al (2020) Active and incremental learning for semantic ALS point cloud segmentation. ISPRS J Photogramm Remote Sens 169:73–92

    Article  Google Scholar 

  • Liu W, Luo X, Huang F et al (2019) Prediction of soil water retention curve using bayesian updating from limited measurement data. Appl Math Model 76:380–395

    Article  Google Scholar 

  • Marcot BG, Penman TD (2019) Advances in Bayesian network modelling: integration of modelling technologies. Environ Model Softw 111:386–393

    Article  Google Scholar 

  • Marjanović M, Kovačević M, Bajat B et al (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123(3):225–234

    Article  Google Scholar 

  • Mebrahtu TK, Hussien B, Banning A et al (2020) Predisposing and triggering factors of large-scale landslides in Debre Sina area, central Ethiopian highlands. Bull Eng Geol Env 80(1):365–383. https://doi.org/10.1007/s10064-020-01961-1

    Article  Google Scholar 

  • Mondal S, Maiti R (2014) Integrating the analytical hierarchy process (AHP) and the frequency ratio (FR) model in landslide susceptibility mapping of Shiv-khola watershed, Darjeeling Himalaya. Int J Disaster Risk Sci 4(4):200–212. https://doi.org/10.1007/s13753-013-0021-y

    Article  Google Scholar 

  • Mukherjee S, Sharma N (2012) Intrusion detection using naive Bayes classifier with feature reduction. Procedia Technol 4:119–128

    Article  Google Scholar 

  • Neaupane KM, Piantanakulchai M (2006) Analytic network process model for landslide hazard zonation. Eng Geol 85(3):281–294. https://doi.org/10.1016/j.enggeo.2006.02.003

    Article  Google Scholar 

  • Obrike SE, Barr SL, Miller PE et al (2021) Engineered slope failure susceptibility modelling using high spatial resolution geospatial data. Bull Eng Geol Env 80(10):7361–7384. https://doi.org/10.1007/s10064-021-02413-0

    Article  Google Scholar 

  • Pham BT, Jaafari A, Prakash I et al (2018) A novel hybrid intelligent model of support vector machines and the multiboost ensemble for landslide susceptibility modeling. Bull Eng Geol Env 78(4):2865–2886. https://doi.org/10.1007/s10064-018-1281-y

    Article  Google Scholar 

  • Pham BT, Nguyen-Thoi T, Qi C et al (2020) Coupling rbf neural network with ensemble learning techniques for landslide susceptibility mapping. CATENA. https://doi.org/10.1016/j.catena.2020.104805

    Article  Google Scholar 

  • Pham BT, Tien Bui D, Pourghasemi HR et al (2015) Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve Bayes, multilayer perceptron neural networks, and functional trees methods. Theoret Appl Climatol 128(1–2):255–273. https://doi.org/10.1007/s00704-015-1702-9

    Article  Google Scholar 

  • Polikar R, Upda L, Upda SS et al (2001) Learn++: an incremental learning algorithm for supervised neural networks. IEEE Trans Syst Man Cybern C Appl Rev 31(4):497–508

    Article  Google Scholar 

  • Pradhan B (2010) Remote sensing and gis-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Adv Space Res 45(10):1244–1256

    Article  Google Scholar 

  • Reichenbach P, Rossi M, Malamud BD et al (2018) A review of statistically-based landslide susceptibility models. Earth Sci Rev 180:60–91

    Article  Google Scholar 

  • Romali NS, Yusop Z (2021) Flood damage and risk assessment for urban area in Malaysia. Hydrol Res 52(1):142–159. https://doi.org/10.2166/nh.2020.121

    Article  Google Scholar 

  • Shao X, Ma S, Xu C et al (2019) Planet image-based inventorying and machine learning-based susceptibility mapping for the landslides triggered by the 2018 Mw6.6 Tomakomai, Japan earthquake. Remote Sens 11(8):978. https://doi.org/10.3390/rs11080978

  • Shirzadi A, Soliamani K, Habibnejhad M et al (2018) Novel GIS based machine learning algorithms for shallow landslide susceptibility mapping. Sensors 18(11):3777. https://doi.org/10.3390/s18113777

    Article  Google Scholar 

  • Song Y, Gong J, Gao S et al (2012) Susceptibility assessment of earthquake-induced landslides using Bayesian network: a case study in Beichuan, China. Comput Geosci 42:189–199. https://doi.org/10.1016/j.cageo.2011.09.011

    Article  Google Scholar 

  • Su C, Wang L, Wang X et al (2015) Mapping of rainfall-induced landslide susceptibility in Wencheng, China, using support vector machine. Nat Hazards 76(3):1759–1779. https://doi.org/10.1007/s11069-014-1562-0

    Article  Google Scholar 

  • Tang R-X, Kulatilake PHSW, Yan EC et al (2020) Evaluating landslide susceptibility based on cluster analysis, probabilistic methods, and artificial neural networks. Bull Eng Geol Env 79(5):2235–2254. https://doi.org/10.1007/s10064-019-01684-y

    Article  Google Scholar 

  • Tien Bui D, Tuan TA, Klempe H et al (2015) 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 tree. Landslides 13(2):361–378. https://doi.org/10.1007/s10346-015-0557-6

    Article  Google Scholar 

  • Wang HJ, Xiao T, Li XY et al (2019) A novel physically-based model for updating landslide susceptibility. Eng Geol 251:71–80. https://doi.org/10.1016/j.enggeo.2019.02.004

    Article  Google Scholar 

  • Wang X, Li S, Liu H et al (2021) Landslide susceptibility assessment in Wenchuan County after the 5.12 magnitude earthquake. Bull Eng Geol Environ 80(7):5369–5390. https://doi.org/10.1007/s10064-021-02280-9

    Article  Google Scholar 

  • Wu H, Xu Z, Yan W et al (2019) Incremental learning introspective movement primitives from multimodal unstructured demonstrations. IEEE Access 7:159022–159036

    Article  Google Scholar 

  • Wu Y, Ke Y, Chen Z et al (2020) Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping. CATENA 187:104396. https://doi.org/10.1016/j.catena.2019.104396

    Article  Google Scholar 

  • Xu C, Dai FC, Xu XW et al (2012) GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology 146:70–80

    Article  Google Scholar 

  • Xu J, Xu C, Zou B et al (2019a) New incremental learning algorithm with support vector machines. IEEE Trans Syst Man Cybern Syst 49(11):2230–2241. https://doi.org/10.1109/TSMC.2018.2791511

    Article  Google Scholar 

  • Xu X, Lu J, Zhang N et al (2019b) Inversion of rice canopy chlorophyll content and leaf area index based on coupling of radiative transfer and Bayesian network models. ISPRS J Photogramm Remote Sens 150:185–196

    Article  Google Scholar 

  • Yang Y, Wu W, Zheng H (2020) Searching for critical slip surfaces of slopes using stress fields by numerical manifold method. J Rock Mech Geotech Eng 12(6):1313–1325. https://doi.org/10.1016/j.jrmge.2020.03.006

    Article  Google Scholar 

  • Yang Y, Wu W, Zheng H (2021) Stability analysis of slopes using the vector sum numerical manifold method. Bull Eng Geol Env 80(1):345–352. https://doi.org/10.1007/s10064-020-01903-x

    Article  Google Scholar 

  • Youssef AM, Pourghasemi HR, El-Haddad BA et al (2016) Landslide susceptibility maps using different probabilistic and bivariate statistical models and comparison of their performance at Wadi Itwad Basin, Asir region, Saudi Arabia. Bull Eng Geol Env 75(1):63–87

    Article  Google Scholar 

  • Youssef AM, Pourghasemi HR, Pourtaghi ZS et al (2015) 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 Arabia. Landslides 13(5):839–856. https://doi.org/10.1007/s10346-015-0614-1

    Article  Google Scholar 

  • Yu H (2019) Incremental learning of Bayesian networks from concept-drift data. In: 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

  • Zhang B, Zhang L, Yang H et al (2016) Subsidence prediction and susceptibility zonation for collapse above goaf with thick alluvial cover: a case study of the Yongcheng coalfield, Henan Province, China. Bull Eng Geol Env 75(3):1–16

    Article  Google Scholar 

  • Zhu L, Huang L, Fan L et al (2020) Landslide susceptibility prediction modeling based on remote sensing and a novel deep learning algorithm of a cascade-parallel recurrent neural network. Sensors 20(6):1576. https://doi.org/10.3390/s20061576

Download references

Funding

This research is funded by the National Natural Science Foundation of China (No. 41807285), the China Postdoctoral Science Foundation (No. 2019M652287 and 2020T130274), the Jiangxi Provincial Natural Science Foundation (No. 20192BAB216034), the Jiangxi Provincial Postdoctoral Science Foundation (No. 2019KY08), and the open Foundation of the State Key Laboratory of Water Resources and Hydropower Engineering Science (Wuhan University) (NO.2020SGG04).

Author information

Authors and Affiliations

  1. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan City, Hubei Province, 430072, China

    Faming Huang

  2. School of Civil Engineering and Architecture, Nanchang University, Nanchang, 330031, China

    Faming Huang, Zhou Ye & Chuangbing Zhou

  3. Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Jiangxi, Nanchang, 330013, China

    Xiaoting Zhou

  4. Discipline of Civil, Surveying and Environmental Engineering, Priority Research Centre for Geotechnical Science and Engineering, University of Newcastle, Newcastle, NSW, Australia

    Jinsong Huang

Authors
  1. Faming Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhou Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaoting Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jinsong Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chuangbing Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoting Zhou.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, F., Ye, Z., Zhou, X. et al. Landslide susceptibility prediction using an incremental learning Bayesian Network model considering the continuously updated landslide inventories. Bull Eng Geol Environ 81, 250 (2022). https://doi.org/10.1007/s10064-022-02748-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10064-022-02748-2

Keywords

Search

Navigation