Skip to main content

Advertisement

SpringerLink
Log in

Integration of rotation forest and multiboost ensemble methods with forest by penalizing attributes for spatial prediction of landslide susceptible areas

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

Landslides are among the most destructive natural hazards causing loss of life, destruction of infrastrucures and damage to properties, especially in hilly and mountaneous areas all over the world. To properly plan and manage these regions, it is crucial to accurately identify areas susceptible to landslides. In this study, we developed and applied novel hybrid machine learning models, RF-FPA and MAB-FPA, which combine forest by penalizing attribute (FPA), with two ensemble techniques, random forest (RF) and multiboost (MAB), to construct landslide susceptibility maps of Dien Bien province in Vietnam. For the development of hybrid models, we have used data of 665 landslide events and 12 landslide conditioning factors namely distance to rivers, elevations, distance to roads, normalized difference vegetation index, faults, curvature, slope, flow accumulation, stream power index, geology, topographic wetness index (TWI), and aspect. Standard statistical measures including area under the receiver operating characteristic (AUC-ROC) curve were used to evaluate predictive performance of the developed models. Results showed that both proposed novel hybrid models performed well in the correct identification of landslide susceptible areas, with RF-FPA (AUC = 0.840) slightly outperforming MAB-FPA (AUC = 0.814). The performance of the proposed hybrid models was also significantly better than that of single base classifier FPA (AUC = 0.807). In conclusion, while all the studied models performed well, the novel RF-FPA model is a more promissing tool for accurate mapping and correct prediction of landslide susceptibile areas compared to MAB-FPA amd FPA models. This approach of integrating RF with FPA can also be applied to spatially predict landslides in other areas, considering local geo-environmental conditions for proper management of landslide-prone areas.

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
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

The data and materials will be made available at request.

References

  • Abedini M, Ghasemian B, Shirzadi A, Bui DT (2019) A comparative study of support vector machine and logistic model tree classifiers for shallow landslide susceptibility modeling. Environ Earth Sci 78(18):1–15

    CAS  Google Scholar 

  • Adnan MN, Islam MZ (2017) Forest PA: constructing a decision forest by penalizing attributes used in previous trees. Expert Syst Appl 89:389–403

    Google Scholar 

  • Ado M, Amitab K, Maji AK, Jasińska E, Gono R, Leonowicz Z, Jasiński M (2022) Landslide susceptibility mapping using machine learning: a literature survey. Remote Sens 14(13):3029

    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):1–20

    Google Scholar 

  • Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1–2):15–31

    Google Scholar 

  • Bagnall A, Flynn M, Large J, Line J, Bostrom A, Cawley G (2018) Is rotation forest the best classifier for problems with continuous features? arXiv preprint arXiv:180906705

  • Bien TX, Truyen PT, Phong TV, Nguyen DD, Amiri M, Costache R, Duc DM, Le HV, Nguyen HBT, Prakash I (2022) Landslide susceptibility mapping at sin Ho, Lai Chau province, Vietnam using ensemble models based on fuzzy unordered rules induction algorithm. Geocarto Int 37:1–22

    Google Scholar 

  • Broothaerts N, Kissi E, Poesen J, Van Rompaey A, Getahun K, Van Ranst E, Diels J (2012) Spatial patterns, causes and consequences of landslides in the Gilgel Gibe catchment, SW Ethiopia. CATENA 97:127–136

    Google Scholar 

  • Bui DT, Moayedi H, Kalantar B, Osouli A, Pradhan B, Nguyen H, Rashid ASA (2019) A novel swarm intelligence—Harris hawks optimization for spatial assessment of landslide susceptibility. Sensors 19(16):3590

    Google Scholar 

  • Canavesi V, Segoni S, Rosi A, Ting X, Nery T, Catani F, Casagli N (2020) Different approaches to use morphometric attributes in landslide susceptibility mapping based on meso-scale spatial units: a case study in Rio de Janeiro (Brazil). Remote Sens 12(11):1826

  • Catani F, Lagomarsino D, Segoni S, Tofani V (2013) Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Nat Hazard 13(11):2815–2831

    Google Scholar 

  • Che VB, Kervyn M, Suh CE, Fontijn K, Ernst G, Del Marmol M-A, Trefois P, Jacobs P (2012) Landslide susceptibility assessment in Limbe (SW Cameroon): a field calibrated seed cell and information value method. CATENA 92:83–98

    Google Scholar 

  • Chen W, Li Y (2020) GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models. CATENA 195:104777

    Google Scholar 

  • Chen W, Panahi M, Pourghasemi HR (2017a) Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. CATENA 157:310–324

    Google Scholar 

  • Chen W, Pourghasemi HR, Kornejady A, Zhang N (2017b) Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma 305:314–327

    Google Scholar 

  • Chen W, Pourghasemi HR, Zhao Z (2017c) A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping. Geocarto Int 32(4):367–385

    Google Scholar 

  • Chen W, Xie X, Peng J, Wang J, Duan Z, Hong H (2017d) GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models. Geomat Nat Haz Risk 8(2):950–973

    Google Scholar 

  • Chen W, Peng J, Hong H, Shahabi H, Pradhan B, Liu J, Zhu A-X, Pei X, Duan Z (2018) Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Sci Total Environ 626:1121–1135

    CAS  Google Scholar 

  • Chen W, Zhao X, Shahabi H, Shirzadi A, Khosravi K, Chai H, Zhang S, Zhang L, Ma J, Chen Y (2019) Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree. Geocarto Int 34(11):1177–1201

    Google Scholar 

  • Dehnavi A, Aghdam IN, Pradhan B, Varzandeh MHM (2015) A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. CATENA 135:122–148

    Google Scholar 

  • Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, Chen C-W, Khosravi K, Yang Y, Pham BT (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

    CAS  Google Scholar 

  • Du P, Samat A, Waske B, Liu S, Li Z (2015) Random forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features. ISPRS J Photogramm Remote Sens 105:38–53

    Google Scholar 

  • Fang Z, Wang Y, Duan G, Peng L (2021) Landslide susceptibility mapping using rotation forest ensemble technique with different decision trees in the Three Gorges Reservoir area, China. Remote Sens 13(2):238

    Google Scholar 

  • Ghasemain B, Asl DT, Pham BT, Avand M, Nguyen HD, Janizadeh S (2020) Shallow landslide susceptibility mapping: a comparison between classification and regression tree and reduced error pruning tree algorithms. Vietnam J Earth Sci 42(3):208–227

    Google Scholar 

  • Gorsevski PV, Brown MK, Panter K, Onasch CM, Simic A, Snyder J (2016) Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: a case study in the Cuyahoga Valley National Park, Ohio. Landslides 13(3):467–484

    Google Scholar 

  • Guru B, Veerappan R, Sangma F, Bera S (2017) Comparison of probabilistic and expert-based models in landslide susceptibility zonation mapping in part of Nilgiri District, Tamil Nadu, India. Spat Inf Res 25(6):757–768

    Google Scholar 

  • Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang K-T (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112(1–2):42–66

    Google Scholar 

  • Hai HD, Ngo HTT, Van PT, Duc DN, Avand M, Huu DN, Amiri M, Van Le H, Prakash I, Thai PB (2022) Development and application of hybrid artificial intelligence models for groundwater potential mapping and assessment. Vietnam J Earth Sci 44(3):375–393

    Google Scholar 

  • Hakim WL, Rezaie F, Nur AS, Panahi M, Khosravi K, Lee C-W, Lee S (2022) Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea. J Environ Manag 305:114367

    Google Scholar 

  • Hall MA (2000) Correlation-based feature selection of discrete and numeric class machine learning. Woring paper series

  • He S, Pan P, Dai L, Wang H, Liu J (2012) Application of Kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China. Geomorphology 171:30–41

    Google Scholar 

  • He Q, Shahabi H, Shirzadi A, Li S, Chen W, Wang N, Chai H, Bian H, Ma J, Chen Y (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

    CAS  Google Scholar 

  • Hong H, Chen W, Xu C, Youssef AM, Pradhan B, Tien Bui D (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

    Google Scholar 

  • Hong H, Liu J, Bui DT, Pradhan B, Acharya TD, Pham BT, Zhu A-X, Chen W, Ahmad BB (2018) Landslide susceptibility mapping using J48 decision tree with AdaBoost, bagging and rotation forest ensembles in the Guangchang area (China). CATENA 163:399–413

    Google Scholar 

  • Hong H, Liu J, Zhu A-X (2020) Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble. Sci Total Environ 718:137231

    CAS  Google Scholar 

  • Huang F, Pan L, Fan X, Jiang S-H, Huang J, Zhou C (2022) The uncertainty of landslide susceptibility prediction modeling: suitability of linear conditioning factors. Bull Eng Geol Environ 81(5):182

    Google Scholar 

  • Iqbal M, Zhang D, Jalal FE (2021) Durability evaluation of GFRP rebars in harsh alkaline environment using optimized tree-based random forest model. J Ocean Eng Sci. https://doi.org/10.1016/j.joes.2021.10.012

    Article  Google Scholar 

  • Kanungo D, Arora M, Sarkar S, Gupta R (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85(3–4):347–366

    Google Scholar 

  • Kanungo D, Sarkar S, Sharma S (2011) Combining neural network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslides. Nat Hazards 59(3):1491

    Google Scholar 

  • Komac M (2006) A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74(1–4):17–28

    Google Scholar 

  • Kotti M, Benetos E, Kotropoulos C, Pitas I (2007) A neural network approach to audio-assisted movie dialogue detection. Neurocomputing 71(1–3):157–166

    Google Scholar 

  • Kuncheva LI (2014) Combining pattern classifiers: methods and algorithms. Wiley

    Google Scholar 

  • Le H-A, Nguyen T-A, Nguyen D-D, Prakash I (2020) Prediction of soil unconfined compressive strength using artificial neural network model. Vietnam J Earth Sci 42(3):255–264

    Google Scholar 

  • Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26(7):1477–1491

    Google Scholar 

  • 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(1):413–429

    Google Scholar 

  • Lee D-H, Kim Y-T, Lee S-R (2020) Shallow landslide susceptibility models based on artificial neural networks considering the factor selection method and various non-linear activation functions. Remote Sens 12(7):1194

    Google Scholar 

  • Li C, Fu Z, Wang Y, Tang H, Yan J, Gong W, Yao W, Criss RE (2019) Susceptibility of reservoir-induced landslides and strategies for increasing the slope stability in the Three Gorges Reservoir Area: Zigui Basin as an example. Eng Geol 261:105279

    Google Scholar 

  • Liu Z, Gilbert G, Cepeda JM, Lysdahl AOK, Piciullo L, Hefre H, Lacasse S (2021) Modelling of shallow landslides with machine learning algorithms. Geosci Front 12(1):385–393

    CAS  Google Scholar 

  • Merghadi A, Yunus AP, Dou J, Whiteley J, ThaiPham B, Bui DT, Avtar R, Abderrahmane B (2020) Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance. Earth Sci Rev 207:103225

    Google Scholar 

  • Nguyen VV, Pham BT, Vu BT, Prakash I, Jha S, Shahabi H, Shirzadi A, Ba DN, Kumar R, Chatterjee JM (2019) Hybrid machine learning approaches for landslide susceptibility modeling. Forests 10(2):157

    Google Scholar 

  • Nhu V-H, Mohammadi A, Shahabi H, Ahmad BB, Al-Ansari N, Shirzadi A, Clague JJ, Jaafari A, Chen W, Nguyen H (2020a) Landslide susceptibility mapping using machine learning algorithms and remote sensing data in a tropical environment. Int J Environ Res Public Health 17(14):4933

    Google Scholar 

  • Nhu V-H, Shirzadi A, Shahabi H, Singh SK, Al-Ansari N, Clague JJ, Jaafari A, Chen W, Miraki S, Dou J (2020b) Shallow landslide susceptibility mapping: a comparison between logistic model tree, logistic regression, naïve bayes tree, artificial neural network, and support vector machine algorithms. Int J Environ Res Public Health 17(8):2749

    Google Scholar 

  • Nhu V-H, Bui TT, My LN, Vuong H, Duc HN (2022) A new approach based on integration of random subspace and C4 5 decision tree learning method for spatial prediction of shallow landslides. Vietnam J Earth Sci 44(3):327–342

    Google Scholar 

  • Pardo C, Diez-Pastor JF, García-Osorio C, Rodríguez JJ (2013) Rotation forests for regression. Appl Math Comput 219(19):9914–9924

    Google Scholar 

  • Park S, Hamm S-Y, Kim J (2019) Performance evaluation of the GIS-based data-mining techniques decision tree, random forest, and rotation forest for landslide susceptibility modeling. Sustainability 11(20):5659

    Google Scholar 

  • Peng L, Niu R, Huang B, Wu X, Zhao Y, Ye R (2014) Landslide susceptibility mapping based on rough set theory and support vector machines: a case of the Three Gorges area, China. Geomorphology 204:287–301

    Google Scholar 

  • Pham BT, Prakash I, Jaafari A, Bui DT (2018a) Spatial prediction of rainfall-induced landslides using aggregating one-dependence estimators classifier. J Indian Soc Remote Sens 46(9):1457–1470

    Google Scholar 

  • Pham BT, Tien Bui D, Prakash I (2018b) Bagging based support vector machines for spatial prediction of landslides. Environ Earth Sci 77:1–17

    Google Scholar 

  • Pham BT, Jaafari A, Prakash I, Bui DT (2019a) A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling. Bull Eng Geol Env 78:2865–2886

    Google Scholar 

  • Pham BT, Prakash I, Singh SK, Shirzadi A, Shahabi H, Bui DT (2019b) Landslide susceptibility modeling using reduced error pruning trees and different ensemble techniques: hybrid machine learning approaches. CATENA 175:203–218

    Google Scholar 

  • Pham BT, Amiri M, Nguyen MD, Ngo TQ, Nguyen KT, Tran HT, Vu H, Anh BTQ, Van Le H, Prakash I (2021) Estimation of shear strength parameters of soil using optimized inference intelligence system. Vietnam J Earth Sci 43(2):189–198

    Google Scholar 

  • Pham BT, Jaafari A, Nguyen DD, Bayat M, Nguyen HBT (2022a) Development of multiclass alternating decision trees based models for landslide susceptibility mapping. Phys Chem Earth Parts a/b/c 128:103235

    Google Scholar 

  • Pham BT, Vu VD, Costache R, Phong TV, Ngo TQ, Tran T-H, Nguyen HD, Amiri M, Tan MT, Trinh PT (2022b) Landslide susceptibility mapping using state-of-the-art machine learning ensembles. Geocarto Int 37(18):5175–5200

    Google Scholar 

  • 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(1):249–274

    Google Scholar 

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

    Google Scholar 

  • Rodriguez JJ, Kuncheva LI, Alonso CJ (2006) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28(10):1619–1630

    Google Scholar 

  • Saha S, Saha A, Hembram TK, Kundu B, Sarkar R (2022) Novel ensemble of deep learning neural network and support vector machine for landslide susceptibility mapping in Tehri region, Garhwal Himalaya. Geocarto Int 37:1–26

    Google Scholar 

  • Shano L, Raghuvanshi TK, Meten M (2021) Landslide susceptibility mapping using frequency ratio model: the case of Gamo highland, South Ethiopia. Arab J Geosci 14(7):1–18

    Google Scholar 

  • Sun X, Chen J, Han X, Bao Y, Zhan J, Peng W (2020) Application of a GIS-based slope unit method for landslide susceptibility mapping along the rapidly uplifting section of the upper Jinsha River, South-Western China. Bull Eng Geol Env 79:533–549

    Google Scholar 

  • Sur U, Singh P, Meena SR (2020) Landslide susceptibility assessment in a lesser Himalayan road corridor (India) applying fuzzy AHP technique and earth-observation data. Geomat Nat Haz Risk 11(1):2176–2209

    Google Scholar 

  • Thanh DQ, Nguyen DH, Prakash I, Jaafari A, Nguyen V-T, Van Phong T, Pham BT (2020) GIS based frequency ratio method for landslide susceptibility mapping at Da Lat City, Lam Dong province, Vietnam. Vietnam J Earth Sci 42(1):55–66

    Google Scholar 

  • Thomas AV, Saha S, Danumah JH, Raveendran S, Prasad MK, Ajin R, Kuriakose SL (2021) Landslide susceptibility zonation of Idukki District using GIS in the aftermath of 2018 Kerala floods and landslides: a comparison of AHP and frequency ratio methods. J Geovisual Spat Anal 5(2):1–27

    Google Scholar 

  • Tien Bui D, Hoang N-D (2017) A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1. 1) for spatial prediction of floods. Geosci Model Dev 10(9):3391–3409

    Google Scholar 

  • Tiranti D, Cavalli M, Crema S, Zerbato M, Graziadei M, Barbero S, Cremonini R, Silvestro C, Bodrato G, Tresso F (2016) Semi-quantitative method for the assessment of debris supply from slopes to river in ungauged catchments. Sci Total Environ 554:337–348

    Google Scholar 

  • Tran T-H, Dam ND, Jalal FE, Al-Ansari N, Ho LS, Phong TV, Iqbal M, Le HV, Nguyen HBT, Prakash I, Pham BT (2021) GIS-based soft computing models for landslide susceptibility mapping: a case study of Pithoragarh District, Uttarakhand State, India. Math Probl Eng 2021:9914650. https://doi.org/10.1155/2021/9914650

    Article  Google Scholar 

  • UNISDR C (2015) The human cost of natural disasters: a global perspective

  • Van Phong T, Ly H-B, Trinh PT, Prakash I, Btjvjoes P (2020) Landslide susceptibility mapping using forest by penalizing attributes (FPA) algorithm based machine learning approach. Vietnam J Earth Sci 42(3):237–246

    Google Scholar 

  • Wang L-J, Guo M, Sawada K, Lin J, Zhang J (2015) Landslide susceptibility mapping in Mizunami City, Japan: a comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models. CATENA 135:271–282

    Google Scholar 

  • Wang G, Lei X, Chen W, Shahabi H, Shirzadi A (2020) Hybrid computational intelligence methods for landslide susceptibility mapping. Symmetry 12(3):325

    Google Scholar 

  • Webb GI (2000) Multiboosting: a technique for combining boosting and wagging. Mach Learn 40(2):159

    Google Scholar 

  • Wubalem A, Meten M (2020) Landslide susceptibility mapping using information value and logistic regression models in Goncha Siso Eneses area, northwestern Ethiopia. SN Appl Sci 2(5):1–19

    Google Scholar 

  • Xi C, Han M, Hu X, Liu B, He K, Luo G, Cao X (2022) Effectiveness of Newmark-based sampling strategy for coseismic landslide susceptibility mapping using deep learning, support vector machine, and logistic regression. Bull Eng Geol Environ 81(5):174

    Google Scholar 

  • Youssef AM, Pourghasemi HR (2021) Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia. Geosci Front 12(2):639–655

    Google Scholar 

  • Zhang K, Wu X, Niu R, Yang K, Zhao L (2017) The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area, China. Environ Earth Sci 76(11):1–20

    Google Scholar 

  • Zhang H, Song Y, Xu S, He Y, Li Z, Yu X, Liang Y, Wu W, Wang Y (2022) Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: a case study of Wanzhou section of the Three Gorges Reservoir, China. Comput Geosci 158:104966

    Google Scholar 

  • Zhou C, Yin K, Cao Y, Ahmed B, Li Y, Catani F, Pourghasemi HR (2018) Landslide susceptibility modeling applying machine learning methods: a case study from Longju in the Three Gorges Reservoir area, China. Comput Geosci 112:23–37

    Google Scholar 

Download references

Acknowledgements

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 105.08-2019.03.

Author information

Authors and Affiliations

  1. Hanoi University of Natural Resources and Environment, Hanoi, Vietnam

    Tran Xuan Bien

  2. Department of Civil Engineering, University of Engineering and Technology Peshawar, Peshawar, Pakistan

    Mudassir Iqbal

  3. Interdisciplinary Research Center of Smart Mobility and Logistics (IRC-SML), King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia

    Arshad Jamal

  4. University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi, Vietnam

    Dam Duc Nguyen, Lanh Si Ho, Hiep Van Le, Hanh Bich Thi Nguyen & Binh Thai Pham

  5. Institute of Geological Sciences, Vietnam Academy of Science and Technology, 84 Chua Lang Street, Dong Da, Hanoi, 100000, Vietnam

    Tran Van Phong

  6. National Institute of Hydrology and Water Management, 97E Sos. Bucuresti-Ploieşti, 1st District, 013686, Bucharest, Romania

    Romulus Costache

  7. Department of Civil Engineering, Transilvania University of Braşov, 5, Turnului Str, 500152, Brasov, Romania

    Romulus Costache

  8. Danube Delta National Institute for Research and Development, 165 Babadag Street, 820112, Tulcea, Romania

    Romulus Costache

  9. DDG(R) Geological Survey of India, Gandhinagar, Gujarat, 382010, India

    Indra Prakash

  10. Civil and Environmental Engineering Program, Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, 739-8527, Japan

    Binh Thai Pham

Authors
  1. Tran Xuan Bien
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mudassir Iqbal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arshad Jamal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dam Duc Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tran Van Phong
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Romulus Costache
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lanh Si Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hiep Van Le
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Hanh Bich Thi Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Indra Prakash
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Binh Thai Pham
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

BTP, TXB: Conceptualization, Methodology, Software; DDN, TVP, BTP: Data curation; MI, AJ, DDN, TVP, RC, HVL, HBTN, IP, BTP: Writing-Original draft preparation. DDN, TVP, HVL, LSH: Visualization, Investigation. IP, TXB, BTP: Supervision.: DDN, RC, TVP, IP, BTP: Software, Validation: All authors: Writing-Reviewing and Editing.

Corresponding authors

Correspondence to Romulus Costache or Binh Thai Pham.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval and consent to participate

Not applicable.

Consent for publication

When accepted the authors give the journal authority to publish.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bien, T.X., Iqbal, M., Jamal, A. et al. Integration of rotation forest and multiboost ensemble methods with forest by penalizing attributes for spatial prediction of landslide susceptible areas. Stoch Environ Res Risk Assess 37, 4641–4660 (2023). https://doi.org/10.1007/s00477-023-02521-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-023-02521-1

Keywords

Search

Navigation