Top

Bulletin of Engineering Geology and the Environment

Published in:

01-06-2022 | Original Paper

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

Authors: Faming Huang, Zhou Ye, Xiaoting Zhou, Jinsong Huang, Chuangbing Zhou

Published in: Bulletin of Engineering Geology and the Environment | Issue 6/2022

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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.

previous article The whitish variety of Ançã limestone: Evaluation of fire-induced damage using ultrasonic tomography

next article Experimental study on failure mechanism and geometric parameters of blasting crater under uniaxial static compressive stresses

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

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-xCrossRef

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/s12665-015-5233-6CrossRef

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.2020.09.004CrossRef

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/s11069-011-9879-4CrossRef

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):73–81CrossRef

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.2020.125771CrossRef

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 578:124080CrossRef

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/s10064-021-02386-0CrossRef

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-BCrossRef

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-xCrossRef

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):502CrossRef

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.2018.01.124CrossRef

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.2017.09.007CrossRef

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/s10064-018-1401-8CrossRef

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-zCrossRef

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-yCrossRef

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/s10064-019-01590-3CrossRef

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/s10064-021-02197-3CrossRef

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/s10064-020-01915-7CrossRef

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):268–289CrossRef

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/s10064-019-01583-2CrossRef

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

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.2012.02.001CrossRef

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/s10064-019-01506-1CrossRef

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 259:105–118CrossRef

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 191:104580CrossRef

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/s10346-020-01473-9CrossRef

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/s10064-020-01886-9CrossRef

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/10.1080/19475705.2018.1482963CrossRef

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.2021.105250CrossRef

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

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):221–238CrossRef

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

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.2015.04.004CrossRef

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 81:144CrossRef

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.2019.03.073CrossRef

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):3664CrossRef

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-xCrossRef

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/s10064-018-1404-5CrossRef

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 169:73–92CrossRef

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 76:380–395CrossRef

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

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

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/s10064-020-01961-1CrossRef

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-yCrossRef

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

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.2006.02.003CrossRef

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/s10064-021-02413-0CrossRef

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-yCrossRef

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.2020.104805CrossRef

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/s00704-015-1702-9CrossRef

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):497–508CrossRef

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):1244–1256CrossRef

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

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.2020.121CrossRef

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/s18113777CrossRef

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.2011.09.011CrossRef

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/s11069-014-1562-0CrossRef

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-yCrossRef

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/s10346-015-0557-6CrossRef

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.2019.02.004CrossRef

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/s10064-021-02280-9CrossRef

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

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.2019.104396CrossRef

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 146:70–80CrossRef

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.2018.2791511CrossRef

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 150:185–196CrossRef

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.2020.03.006CrossRef

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-xCrossRef

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):63–87CrossRef

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/s10346-015-0614-1CrossRef

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):1–16CrossRef

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

Title: Landslide susceptibility prediction using an incremental learning Bayesian Network model considering the continuously updated landslide inventories
Authors: Faming Huang
Zhou Ye
Xiaoting Zhou
Jinsong Huang
Chuangbing Zhou
Publication date: 01-06-2022
Publisher: Springer Berlin Heidelberg
Published in: Bulletin of Engineering Geology and the Environment / Issue 6/2022
Print ISSN: 1435-9529
Electronic ISSN: 1435-9537
DOI: https://doi.org/10.1007/s10064-022-02748-2

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 6/2022

Failure mechanism of a high-locality colluvial landslide in Wanzhou County, Chongqing, China

Depth-consistent models for probabilistic liquefaction potential assessment based on shear wave velocity

An improved nonlinear creep damage model of slates considering freeze–thaw damage and bedding damage

A data-driven method for predicting debris-flow runout zones by integrating multivariate adaptive regression splines and Akaike information criterion

Particle breakage and shape analysis of calcareous sand under consolidated-undrained triaxial shear

Deep learning–based stochastic modelling and uncertainty analysis of fault networks