Decision tree based ensemble machine learning approaches for landslide susceptibility mapping

References

• Abellán J, Masegosa AR. 2007. Combining decision trees based on imprecise probabilities and uncertainty measures. Berlin, Heidelberg: Springer. p. 512–523.
   Google Scholar
• Abellan J, Moral S. 1999. Completing a total uncertainty measure in the dempster-shafer theory. Int J Gen Syst. 28(4-5):299–314.
   Web of Science ®Google Scholar
• Abellan J, Moral S. 2000. A non-specificity measure for convex sets of probability distributions. Int J Unc Fuzz Knowl Based Syst. 08(03):357–367.
   Google Scholar
• Abellán J, Moral S. 2003. Building classification trees using the total uncertainty criterion. Int J Intell Syst. 18(12):1215–1225.
   Web of Science ®Google Scholar
• Achour Y, Boumezbeur A, Hadji R, Chouabbi A, Cavaleiro V, Bendaoud EA. 2017. Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine. Arab J Geosci. 10(8):194.
   Web of Science ®Google Scholar
• Ali A, Huang J, Lyamin AV, Sloan SW, Griffiths DV, Cassidy MJ, Li JH. 2014. Simplified quantitative risk assessment of rainfall-induced landslides modelled by infinite slopes. Eng Geol. 179:102–116.
   Web of Science ®Google Scholar
• Arabameri A, Chen W, Loche M, Zhao X, Li Y, Lombardo L, Cerda A, Pradhan B, Bui DT. 2020. Comparison of machine learning models for gully erosion susceptibility mapping. Geosci Front. 11(5):1609–1620.
   Web of Science ®Google Scholar
• Arabameri A, Karimi-Sangchini E, Pal SC, Saha A, Chowdhuri I, Lee S, Tien Bui D. 2020. Novel credal decision tree-based ensemble approaches for predicting the landslide susceptibility. Remote Sens. 12(20):3389.
   Google Scholar
• Arabameri A, Pourghasemi HR, Yamani M. 2017. Applying different scenarios for landslide spatial modeling using computational intelligence methods. Environ Earth Sci. 76(24):832.
   Web of Science ®Google Scholar
• Arabameri A, Pradhan B, Bui DT. 2020. Spatial modelling of gully erosion in the Ardib River Watershed using three statistical-based techniques. CATENA. 190:104545.
   Web of Science ®Google Scholar
• Arabameri A, Pradhan B, Lombardo L. 2019. Comparative assessment using boosted regression trees, binary logistic regression, frequency ratio and numerical risk factor for gully erosion susceptibility modelling. CATENA. 183(104223):104223.
   Google Scholar
• Arabameri A, Pradhan B, Rezaei K, Yamani M, Pourghasemi HR, Lombardo L. 2018. Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function–logistic regression algorithm. Land Degrad Dev. 29(11):4035–4049.
   Web of Science ®Google Scholar
• Arabameri A, Saha S, Roy J, Tiefenbacher JP, Cerda A, Biggs T, Pradhan B, Ngo PTT, Collins AL. 2020. A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility. Sci Total Environ. 726:138595.
   PubMed Web of Science ®Google Scholar
• Arabameri A, Yamani M, Pradhan B, Melesse A, Shirani K, Bui DT. 2019. Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility. Sci Total Environ. 688:903–916.
   PubMed Web of Science ®Google Scholar
• Arabameri C, Rodrigo-Comino P, Sohrabi B, Bui T. 2019. Proposing a novel predictive technique for gully erosion susceptibility mapping in arid and semi-arid regions (Iran). Remote Sens. 11(21):2577.
   Google Scholar
• Aristizábal Giraldo EV. 2013. SHIA_Landslide: developing a physically based model to predict shallow landslides triggered by rainfall in tropical environments. Escuela de Geociencias y Medio Ambiente.
   Google Scholar
• Barlow J, Martin Y, Franklin SE. 2003. Detecting translational landslide scars using segmentation of Landsat ETM + and DEM data in the northern Cascade Mountains, British Columbia. Can J Remote Sens. 29(4):510–517.
   Web of Science ®Google Scholar
• Beck A. 2008. Simulation: the practice of model development and use. 1st ed. John Wiley & Sons.
   Google Scholar
• Beven KJ, Kirkby MJ. 1979. A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrol Sci J. 24(1):43–69.
   Google Scholar
• Breiman L. 1996. Bagging predictors. Mach Learn. 24(2):123–140.
   Web of Science ®Google Scholar
• Breiman L. 2001. Random forests. Mach Learn. 45(1):5–32.
   Web of Science ®Google Scholar
• Bui DT, Ho T-C, Pradhan B, Pham B-T, Nhu V-H, Revhaug I. 2016. GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks. Environ Earth Sci. 75(14):1101.
   Web of Science ®Google Scholar
• Bui DT, Lofman O, Revhaug I, Dick O. 2011. Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Nat Hazards. 59(3):1413–1444.
   Web of Science ®Google Scholar
• Cao B, Dong W, Lv Z, Gu Y, Singh S, Kumar P. 2020. Hybrid microgrid many-objective sizing optimization with fuzzy decision. IEEE Trans Fuzzy Syst. 28(11):2702–2710.
   Web of Science ®Google Scholar
• Cao B, Wang X, Zhang W, Song H, Lv Z. 2020. A many-objective optimization model of industrial internet of things based on private blockchain. IEEE Netw. 34(5):78–83.
   Web of Science ®Google Scholar
• Cao B, Zhao J, Lv Z, Gu Y, Yang P, Halgamuge SK. 2020. Multiobjective evolution of fuzzy rough neural network via distributed parallelism for stock prediction. IEEE Trans Fuzzy Syst. 28(5):939–952.
   Web of Science ®Google Scholar
• Carotenuto F, Angrisani AC, Bakthiari A, Carratù MT, Di Martire D, Finicelli GF, Raia P, Calcaterra D. 2017. A new statistical approach for landslide susceptibility assessment in the urban area of Napoli (Italy). In: Mikos M, Tiwari B, Yin Y, Sassa K, editors. Advancing culture of living with landslides. Cham (Switzerland): Springer International Publishing. p. 881–889.
   Google Scholar
• Chakrabortty R, Pal SC, Chowdhuri I, Malik S, Das B. 2020. Assessing the importance of static and dynamic causative factors on erosion potentiality using SWAT, EBF with uncertainty and plausibility, logistic regression and novel ensemble model in a sub-tropical environment. J Indian Soc Remote Sens. 48(5):765–789.
   Web of Science ®Google Scholar
• Chao L, Zhang K, Li Z, Zhu Y, Wang J, Yu Z. 2018. Geographically weighted regression based methods for merging satellite and gauge precipitation. J Hydrol. 558:275–289.
   Web of Science ®Google Scholar
• Chen H, Chen A, Xu L, Xie H, Qiao H, Lin Q, Cai K. 2020. A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources. Agric Water Manage. 240:106303.
   Web of Science ®Google Scholar
• Chen W, Li H, Hou E, Wang S, Wang G, Panahi M, Li T, Peng T, Guo C, Niu C, et al. 2018. GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models. Sci Total Environ. 634:853–867.
   PubMed Web of Science ®Google Scholar
• Chowdhuri I, Pal SC, Chakrabortty R. 2020. Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India. Adv Space Res. 65(5):1466–1489.
   Web of Science ®Google Scholar
• Conoscenti C, Angileri S, Cappadonia C, Rotigliano E, Agnesi V, Märker M. 2014. Gully erosion susceptibility assessment by means of GIS-based logistic regression: a case of Sicily (Italy). Geomorphology. 204:399–411.
   Web of Science ®Google Scholar
• Dai F, Lee C. 2001. Frequency–volume relation and prediction of rainfall-induced landslides. Eng Geol. 59(3-4):253–266.
   Web of Science ®Google Scholar
• Davoodi MH, Shariatmadari N, Samimi M. 2003. Investigation of Barikan landslide. Internat J Indus Engg Prod Manag (IJIE) (International Journal of Engineering Science) (Persian). 14(2): 10-10. https://www.sid.ir/en/journal/ViewPaper.aspx?id=2743
   Google Scholar
• De Vita P, Napolitano E, Godt JW, Baum RL. 2013. Deterministic estimation of hydrological thresholds for shallow landslide initiation and slope stability models: case study from the Somma-Vesuvius area of southern Italy. Landslides. 10(6):713–728.
   Web of Science ®Google Scholar
• Dempster AP. 2008. Upper and lower probabilities induced by a multivalued mapping. In: Classic works of the Dempster-Shafer theory of belief functions. Berlin, Heidelberg: Springer. p. 57–72.
   Google Scholar
• Di Napoli M, Carotenuto F, Cevasco A, Confuorto P, Di Martire D, Firpo M, Pepe G, Raso E, Calcaterra D. 2020. Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability. Landslides. 17(8):1897–1914..
   Web of Science ®Google Scholar
• Dou J, Yunus AP, Merghadi A, Shirzadi A, Nguyen H, Hussain Y, Avtar R, Chen Y, Pham BT, Yamagishi H. 2020. Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning. Sci Total Environ. 720:137320.
   PubMed Web of Science ®Google Scholar
• Feizizadeh B, Blaschke T. 2014. An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping. Int J Geogr Inf Sci. 28(3):610–638.
   PubMed Web of Science ®Google Scholar
• Feng W, Lu H, Yao T, Yu Q. 2020. Drought characteristics and its elevation dependence in the Qinghai–Tibet plateau during the last half-century. Sci Rep. 10(1):1–11.
   PubMed Web of Science ®Google Scholar
• Finlay P, Fell R, Maguire P. 1997. The relationship between the probability of landslide occurrence and rainfall. Can Geotech J. 34(6):811–824.
   Web of Science ®Google Scholar
• Fu X, Yang Y. 2020. Modeling and analysis of cascading node-link failures in multi-sink wireless sensor networks. Reliab Eng Syst Saf. 197:106815.
   Web of Science ®Google Scholar
• Garosi Y, Sheklabadi M, Conoscenti C, Pourghasemi HR, Van Oost K. 2019. Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion. Sci Total Environ. 664:1117–1132.
   PubMed Web of Science ®Google Scholar
• Gislason PO, Benediktsson JA, Sveinsson JR. 2006. Random forests for land cover classification. Pattern Recog Lett. 27(4):294–300.
   Web of Science ®Google Scholar
• Godt JW, Baum RL, Savage WZ, Salciarini D, Schulz WH, Harp EL. 2008. Transient deterministic shallow landslide modeling: requirements for susceptibility and hazard assessments in a GIS framework. Eng Geol. 102(3-4):214–226.
   Web of Science ®Google Scholar
• Goetz JN, Brenning A, Petschko H, Leopold P. 2015. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci. 81:1–11.
   Web of Science ®Google Scholar
• Hair J, Black W, Babin B, Anderson R. 2009. Multivariate data analysis. 7th ed. Vol. 12. Pearson; p. 221–233.
   Google Scholar
• Han C, Zhang B, Chen H, Wei Z, Liu Y. 2019. Spatially distributed crop model based on remote sensing. Agric Water Manage. 218:165–173.
   Web of Science ®Google Scholar
• He Q, Xu Z, Li S, Li R, Zhang S, Wang N, Pham BT, Chen W. 2019. Novel entropy and rotation forest-based credal decision tree classifier for landslide susceptibility modeling. Entropy. 21(2):106.
   PubMed Web of Science ®Google Scholar
• Hidalgo CA, Vega JA, ParraObando M. 2017. Effect of the rainfall infiltration processes on the landslide hazard assessment of unsaturated soils in tropical mountainous regions. Engineering and Mathematical Topics in Rainfall [Internet]. [accessed 2020 Dec 21]. https://www.intechopen.com/books/engineering-and-mathematical-topics-in-rainfall/effect-of-the-rainfall-infiltration-processes-on-the-landslide-hazard-assessment-of-unsaturated-soil.
   Google Scholar
• Ho TK. 1998. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell. 20(8):832–844.
   Web of Science ®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.
   Web of Science ®Google Scholar
• Jaboyedoff M, Michoud C, Derron M-H, Jérémie V, Leibundgut G, Sudmeier-Rieux K, Nadim F, Leroi E. 2016. Human-induced landslides: toward the analysis of anthropogenic changes of the slope environment. In: Avresa S, Cascini L, Picarelli L, Scavia C, editors. Landslides and engineered slopes. Experience, theory and practice. Berlin, Heidelberg: Springer, CRC Press; p. 217–232.
   Google Scholar
• Kornejady A, Ownegh M, Bahremand A. 2017. Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. CATENA. 152:144–162.
   Web of Science ®Google Scholar
• Kotsianti S, Kanellopoulos D. 2007. Combining bagging, boosting and dagging for classification problems. Berlin, Heidelberg: Springer. p. 493–500.
   Google Scholar
• Lei X, Chen W, Avand M, Janizadeh S, Kariminejad N, Shahabi H, Costache R, Shahabi H, Shirzadi A, Mosavi A. 2020. GIS-based machine learning algorithms for gully erosion susceptibility mapping in a semi-arid region of Iran. Remote Sens. 12(15):2478.
   Web of Science ®Google Scholar
• Li T, Xu M, Zhu C, Yang R, Wang Z, Guan Z. 2019. A deep learning approach for multi-frame in-loop filter of HEVC. IEEE Trans Image Process. 28(11):5663–5678.
   PubMed Web of Science ®Google Scholar
• Liao D, Valliant R. 2012. Variance inflation factors in the analysis of complex survey data. Survey Methodol. 38(1):53–62.
   Web of Science ®Google Scholar
• Liu S, Chan FT, Ran W. 2016. Decision making for the selection of cloud vendor: an improved approach under group decision-making with integrated weights and objective/subjective attributes. Expert Syst Appl. 55:37–47.
   Web of Science ®Google Scholar
• Liu S, Yu W, Chan FT, Niu B. 2021. A variable weight-based hybrid approach for multi-attribute group decision making under interval-valued intuitionistic fuzzy sets. Int J Intell Syst. 36(2):1015–1052.
   Web of Science ®Google Scholar
• Lombardo L, Opitz T, Huser R. 2018. Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster. Stoch Environ Res Risk Assess. 32(7):2179–2198.
   Web of Science ®Google Scholar
• Mantas CJ, Abellán J. 2014. Analysis and extension of decision trees based on imprecise probabilities: application on noisy data. Expert Syst Appl. 41(5):2514–2525.
   Web of Science ®Google Scholar
• Merghadi A, Abderrahmane B, Tien Bui D. 2018. Landslide susceptibility assessment at Mila Basin (Algeria): a comparative assessment of prediction capability of advanced machine learning methods. IJGI. 7(7):268.
   Google Scholar
• Moral-García S, Mantas CJ, Castellano JG, Benítez MD, Abellán J. 2020. Bagging of credal decision trees for imprecise classification. Expert Syst Appl. 141:112944.
   Web of Science ®Google Scholar
• Nsengiyumva JB, Luo G, Nahayo L, Huang X, Cai P. 2018. Landslide susceptibility assessment using spatial multi-criteria evaluation model in Rwanda. IJERPH. 15(2):243.
   Web of Science ®Google Scholar
• Oh H-J, Lee S. 2017. Shallow landslide susceptibility modeling using the data mining models artificial neural network and boosted tree. Applied Sciences. 7(10):1000.
   Google Scholar
• Oh H-J, Lee S, Hong S-M. 2017. Landslide susceptibility assessment using frequency ratio technique with iterative random sampling. J Sens. 2017:e3730913.
   Web of Science ®Google Scholar
• Pal M. 2005. Random forest classifier for remote sensing classification. Int J Remote Sens. 26(1):217–222.
   Web of Science ®Google Scholar
• Pal SC, Arabameri A, Blaschke T, Chowdhuri I, Saha A, Chakrabortty R, Lee S, Band SS. 2020. Ensemble of machine-learning methods for predicting gully erosion susceptibility. Remote Sens. 12(22):3675.
   Google Scholar
• Pal SC, Chowdhuri I. 2019. GIS-based spatial prediction of landslide susceptibility using frequency ratio model of Lachung River basin, North Sikkim, India. SN Appl Sci. 1(5):416.
   Web of Science ®Google Scholar
• Pal SC, Das B, Malik S. 2019. Potential landslide vulnerability zonation using integrated analytic hierarchy process and GIS technique of Upper Rangit Catchment Area, West Sikkim, India. J Indian Soc Remote Sens. 47(10):1643–1655.
   Web of Science ®Google Scholar
• Park HJ, Jang JY, Lee JH. 2019. Assessment of rainfall-induced landslide susceptibility at the regional scale using a physically based model and fuzzy-based Monte Carlo simulation. Landslides. 16(4):695–713.
   Web of Science ®Google Scholar
• Pawluszek K. 2019. Landslide features identification and morphology investigation using high-resolution DEM derivatives. Nat Hazards. 96(1):311–330.
   Web of Science ®Google Scholar
• Pham BT, Bui DT, Pham HV, Le HQ, Prakash I, Dholakia M. 2017. Landslide hazard assessment using random subspace fuzzy rules based classifier ensemble and probability analysis of rainfall data: a case study at Mu Cang Chai District, Yen Bai Province (Viet Nam). J Indian Soc Remote Sens. 45(4):673–683.
   Web of Science ®Google Scholar
• Pham BT, Khosravi K, Prakash I. 2017. Application and comparison of decision tree-based machine learning methods in landside susceptibility assessment at Pauri Garhwal Area, Uttarakhand, India. Environ Process. 4(3):711–730.
   Google Scholar
• Pham BT, Phong TV, Nguyen-Thoi T, Trinh PT, Tran QC, Ho LS, Singh SK, Duyen TTT, Nguyen LT, Le HQ, et al. 2020. GIS-based ensemble soft computing models for landslide susceptibility mapping. Adv Space Res. 66(6):1303–1320.
   Web of Science ®Google Scholar
• Pham BT, Prakash I, Dou J, Singh SK, Trinh PT, Tran HT, Le TM, Van Phong T, Khoi DK, Shirzadi A, et al. 2020. A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers. Geocarto Int. 35(12):1267–1292.
   Web of Science ®Google Scholar
• Pham BT, Tien Bui D, Prakash I, Dholakia MB. 2017. Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. CATENA. 149:52–63.
   Web of Science ®Google Scholar
• Piao Y, Piao M, Jin CH, Shon HS, Chung J-M, Hwang B, Ryu KH. 2015. A new ensemble method with feature space partitioning for high-dimensional data classification. Math Prob Eng. 2015:1–12.
   Web of Science ®Google Scholar
• Pourghasemi H, Pradhan B, Gokceoglu C, Moezzi KD. 2013. A comparative assessment of prediction capabilities of Dempster–Shafer and weights-of-evidence models in landslide susceptibility mapping using GIS. Geomatics Nat Hazards Risk. 4(2):93–118.
   Web of Science ®Google Scholar
• Pourghasemi HR, Jirandeh AG, Pradhan B, Xu C, Gokceoglu C. 2013. Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. J Earth Syst Sci. 122(2):349–369.
   Web of Science ®Google Scholar
• Pourghasemi HR, Rossi M. 2017. Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods. Theor Appl Climatol. 130(1-2):609–633.
   Web of Science ®Google Scholar
• Pradhan AMS, Kim YT. 2016. Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping. CATENA. 140:125–139.
   Web of Science ®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.
   Web of Science ®Google Scholar
• Pradhan B, Lee S. 2010. Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides. 7(1):13–30.
   Web of Science ®Google Scholar
• Pradhan B, Mansor S, Pirasteh S, Buchroithner MF. 2011. Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model. Int J Remote Sens. 32(14):4075–4087.
   Web of Science ®Google Scholar
• Qian J, Feng S, Tao T, Hu Y, Li Y, Chen Q, Zuo C. 2020. Deep-learning-enabled geometric constraints and phase unwrapping for single-shot absolute 3D shape measurement. APL Photonics. 5(4):046105.
   Web of Science ®Google Scholar
• Qiu T, Shi X, Wang J, Li Y, Qu S, Cheng Q, Cui T, Sui S. 2019. Deep learning: a rapid and efficient route to automatic metasurface design. Adv Sci (Weinh)). 6(12):1900128.
   PubMedGoogle Scholar
• Qu S, Han Y, Wu Z, Raza H. 2020. Consensus modeling with asymmetric cost based on data-driven robust optimization. Group Decis Negotiation. 1–38.DOI:10.1007/s10726-020-09707-w
   Web of Science ®Google Scholar
• Roda-Boluda D, D’Arcy M, Whittaker A, McDonald J. 2017. The effects of lithology and landsliding on hillslope sediment supply: case study from Southern Italy. EGU 2017. 19:4977.
   Google Scholar
• Roda-Boluda DC, D’Arcy M, McDonald J, Whittaker AC. 2018. Lithological controls on hillslope sediment supply: insights from landslide activity and grain size distributions. Earth Surf Process Landforms. 43(5):956–977.
   Web of Science ®Google Scholar
• Roy J, Saha S, Arabameri A, Blaschke T, Bui DT. 2019. A novel ensemble approach for landslide susceptibility mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India. Remote Sens. 11(23):2866.
   Google Scholar
• Roy P, Chakrabortty R, Chowdhuri I, Malik S, Das B, Pal SC. 2020. Development of different machine learning ensemble classifier for gully erosion susceptibility in Gandheswari watershed of West Bengal, India. In: Rout JK, Rout M, Das H, editors. Machine learning for intelligent decision science [Internet]. Singapore (Singapore): Springer Singapore. p. 1–26 [accessed 2020 Jun 25]. http://link.springer.com/10.1007/978-981-15-3689-2_1.
   Google Scholar
• Roy P, Pal SC, Chakrabortty R, Chowdhuri I, Malik S, Das B. 2020. Threats of climate and land use change on future flood susceptibility. J Cleaner Prod. 272:122757.
   Web of Science ®Google Scholar
• Scott P. 1994. Assessment of natural resource use by communities from Mt. Elgon National Park. In: UNDP/Technical Report No 15, 1994. Berlin, Heidelberg: Springer.
   Google Scholar
• Sepe C, Confuorto P, Angrisani AC, Di Martire D, Di Napoli M, Calcaterra D, 2019. Application of a statistical approach to landslide susceptibility map generation in urban settings. In: Shakoor A, Cato K, editors. IAEG/AEG Annual Meeting Proceedings, San Francisco, California, 2018 – Volume 1. Cham (Switzerland): Springer International Publishing. p. 155–162.
   Google Scholar
• Shafer G. 1976. A mathematical theory of evidence.Boston, MA: Springer.
   Google Scholar
• Shahbazi B, Chelgani SC, Matin S. 2017. Prediction of froth flotation responses based on various conditioning parameters by Random Forest method. Colloids Surf A. 529:936–941.
   Google Scholar
• Shi K, Wang J, Tang Y, Zhong S. 2020. Reliable asynchronous sampled-data filtering of T–S fuzzy uncertain delayed neural networks with stochastic switched topologies. Fuzzy Sets Syst. 381:1–25.
   Web of Science ®Google Scholar
• Shi K, Wang J, Zhong S, Tang Y, Cheng J. 2020. Non-fragile memory filtering of TS fuzzy delayed neural networks based on switched fuzzy sampled-data control. Fuzzy Sets Syst. 394:40–64.
   Web of Science ®Google Scholar
• Shirzadi A, Bui DT, Pham BT, Solaimani K, Chapi K, Kavian A, Shahabi H, Revhaug I. 2017. Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environ Earth Sci. 76(2):60.
   Web of Science ®Google Scholar
• Skurichina M, Duin RP. 2002. Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal Appl. 5(2):121–135.
   Web of Science ®Google Scholar
• Soil Conservation and Watershed Research Institute Report. 2001. Landslides stabilization methods project (Barikan Landslide Project).
   Google Scholar
• Summa V, Margiotta S, Colaiacovo R, Giannossi ML. 2014. The influence of the grain-size, mineralogical and geo-chemical composition on the Verdesca landslide. Nat Hazards Earth Syst Sci Discuss. 2:5047–5077.
   Google Scholar
• Tao D, Tang X, Li X, Wu X. 2006. Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans Pattern Anal Mach Intell. 28(7):1088–1099.
   PubMed Web of Science ®Google Scholar
• Tian P, Lu H, Feng W, Guan Y, Xue Y. 2020. Large decrease in streamflow and sediment load of Qinghai–Tibetan Plateau driven by future climate change: a case study in Lhasa River Basin. CATENA. 187:104340.
   Web of Science ®Google Scholar
• Tien Bui D, Hoang N-D, Martínez-Álvarez F, Ngo P-TT, Hoa PV, Pham TD, Samui P, Costache R. 2020. A novel deep learning neural network approach for predicting flash flood susceptibility: a case study at a high frequency tropical storm area. Sci Total Environ. 701:134413.
   PubMed Web of Science ®Google Scholar
• Tien Bui D, Tuan TA, Klempe H, Pradhan B, Revhaug I. 2016. 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.
   Web of Science ®Google Scholar
• Tsai Y-H, Wang J, Chien W-T, Wei C-Y, Wang X, Hsieh S-H. 2019. A BIM-based approach for predicting corrosion under insulation. Autom Constr. 107:102923.
   Web of Science ®Google Scholar
• Walley P. 1996. Inferences from multinomial data: learning about a bag of marbles. J R Stat Soc Ser B (Methodol). 58(1):3–34.
   Web of Science ®Google Scholar
• Wang S, Zhang K, van Beek LPH, Tian X, Bogaard TA. 2020. Physically based landslide prediction over a large region: coupling low-resolution hydrological modelling with high-resolution slope stability assessment. Environ Model & Softw. 124. DOI:10.1016/j.envsoft.2019.104607
   Web of Science ®Google Scholar
• Wang X, Tang X. 2006. Random sampling for subspace face recognition. Int J Comput Vision. 70(1):91–104.
   Web of Science ®Google Scholar
• Webb GI. 2000. Multiboosting: a technique for combining boosting and wagging. Mach Learn. 40(2):159–196.
   Web of Science ®Google Scholar
• van Westen CJ, Castellanos E, Kuriakose SL. 2008. Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol. 102(3-4):112–131.
   Web of Science ®Google Scholar
• van Westen CJ, Rengers N, Soeters R. 2003. Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards. 30(3):399–419.
   Web of Science ®Google Scholar
• Wu C, Wu P, Wang J, Jiang R, Chen M, Wang X. 2020. Critical review of data-driven decision-making in bridge operation and maintenance. Struct Infrastruct Eng. 1–24.DOI:10.1080/15732479.2020.1833946
   Web of Science ®Google Scholar
• Wu T, Cao J, Xiong L, Zhang H. 2019. New stabilization results for semi-Markov chaotic systems with fuzzy sampled-data control. Complexity. 2019:1–15.
   Web of Science ®Google Scholar
• Xu M, Li T, Wang Z, Deng X, Yang R, Guan Z. 2018. Reducing complexity of HEVC: a deep learning approach. IEEE Trans Image Process. 27(10):5044–5059.
   Web of Science ®Google Scholar
• Yalcin A. 2008. GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. CATENA. 72(1):1–12.
   Web of Science ®Google Scholar
• Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T. 2011. A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. CATENA. 85(3):274–287.
   Web of Science ®Google Scholar
• Yan J, Pu W, Zhou S, Liu H, Bao Z. 2020. Collaborative detection and power allocation framework for target tracking in multiple radar system. Inf Fusion. 55:173–183.
   Web of Science ®Google Scholar
• Yilmaz I. 2010. Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci. 61(4):821–836.
   Web of Science ®Google Scholar
• Zhang K, Wang Q, Chao L, Ye J, Li Z, Yu Z, Yang T, Ju Q. 2019. Ground observation-based analysis of soil moisture spatiotemporal variability across a humid to semi-humid transitional zone in China. J Hydrol. 574:903–914.
   Web of Science ®Google Scholar
• Zhang Y, Ge T, Tian W, Liou Y-A. 2019. Debris flow susceptibility mapping using machine-learning techniques in Shigatse Area, China. Remote Sens. 11(23):2801.
   Google Scholar
• Zhu J, Wang X, Wang P, Wu Z, Kim MJ. 2019. Integration of BIM and GIS: geometry from IFC to shapefile using open-source technology. Autom Constr. 102:105–119.
   Web of Science ®Google Scholar
• Zhu J, Wu P, Chen M, Kim MJ, Wang X, Fang T. 2020. Automatically processing IFC clipping representation for BIM and GIS integration at the process level. Appl Sci. 10(6):2009.
   Google Scholar
• Zuo C, Chen Q, Gu G, Feng S, Feng F, Li R, Shen G. 2013. High-speed three-dimensional shape measurement for dynamic scenes using bi-frequency tripolar pulse-width-modulation fringe projection. Opt Lasers Eng. 51(8):953–960.
   Web of Science ®Google Scholar
• Zuo C, Chen Q, Tian L, Waller L, Asundi A. 2015. Transport of intensity phase retrieval and computational imaging for partially coherent fields: the phase space perspective. Opt Lasers Eng. 71:20–32.
   Web of Science ®Google Scholar