Ensemble learning framework for landslide susceptibility mapping: Different basic classifier and ensemble strategy

Achour, 2020, How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?, Geosci. Front., 11, 871, 10.1016/j.gsf.2019.10.001 Ali, 2021, GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms, Geosci. Front., 12, 857, 10.1016/j.gsf.2020.09.004 Allocca, 2021, A novel methodology for Groundwater Flooding Susceptibility assessment through Machine Learning techniques in a mixed-land use aquifer, Sci. Total Environ., 790, 148067 Al-Najjar, 2021, Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks, Geosci. Front., 12, 625, 10.1016/j.gsf.2020.09.002 Althuwaynee, 2014, A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping, Landslides, 11, 1063, 10.1007/s10346-014-0466-0 Altınel, 2015, A corpus-based semantic kernel for text classification by using meaning values of terms, Eng. Appl. Artif. Intel., 43, 54, 10.1016/j.engappai.2015.03.015 Atangana Njock, 2021, Artificial neural network optimized by differential evolution for predicting diameters of jet grouted columns, J. Rock Mech. Geotech. Eng., 13, 1500, 10.1016/j.jrmge.2021.05.009 Balogun, 2021, Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms, Geosci. Front., 12, 101104, 10.1016/j.gsf.2020.10.009 Breiman, 1996, Bagging predictors, Machine Learning, 24, 123, 10.1007/BF00058655 Breiman, 2001, Random forests, Machine Learning, 45, 5, 10.1023/A:1010933404324 Buscema, 2002, A brief overview and introduction to artificial neural networks, Subst. Use Misuse, 37, 1093, 10.1081/JA-120004171 Calvello, 2017, Combined use of statistical and DInSAR data analyses to define the state of activity of slow-moving landslides, Landslides, 14, 473, 10.1007/s10346-016-0722-6 Cavazzi, 2013, Are fine resolution digital elevation models always the best choice in digital soil mapping?, Geoderma, 195–196, 111, 10.1016/j.geoderma.2012.11.020 Chae, 2017, Landslide prediction, monitoring and early warning: a concise review of state-of-the-art, Geosci. J., 21, 1033, 10.1007/s12303-017-0034-4 Chen, T., Guestrin, C., 2016. XGBoost: A Scalable Tree Boosting System, In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16)., New York, NY, USA: Association for Computing Machinery, pp. 785–794. Chen, 2021, Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer, Geosci. Front., 12, 93, 10.1016/j.gsf.2020.07.012 Chen, 2021, Landslide susceptibility assessment using weights-of-evidence model and cluster analysis along the highways in the Hubei section of the Three Gorges Reservoir Area, Comput. Geosci., 156, 10.1016/j.cageo.2021.104899 Chen, 2018, GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models, Sci. Total Environ., 634, 853, 10.1016/j.scitotenv.2018.04.055 Ciurleo, 2019, Landslide susceptibility assessment by TRIGRS in a frequently affected shallow instability area, Landslides, 16, 175, 10.1007/s10346-018-1072-3 Corominas, 2014, Recommendations for the quantitative analysis of landslide risk, Bull. Eng. Geol. Environ., 73, 209 Di Napoli, 2020, Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability, Landslides, 17, 1897, 10.1007/s10346-020-01392-9 Dou, 2020, Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan, Landslides, 17, 641, 10.1007/s10346-019-01286-5 Douglas, 2007, Physical vulnerability modelling in natural hazard risk assessment, Nat. Hazards Earth Syst. Sci., 7, 283, 10.5194/nhess-7-283-2007 Elbaz, 2021, Prediction of Disc Cutter Life During Shield Tunneling with AI via the Incorporation of a Genetic Algorithm into a GMDH-Type Neural Network, Engineering, 7, 238, 10.1016/j.eng.2020.02.016 Fang, 2020, Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping., Comput. Geosci., 139, 104470 Fang, 2021, A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping, Int. J. Geogr. Inform. Sci. : IJGIS, 35, 321, 10.1080/13658816.2020.1808897 Fell, 2008, Guidelines for landslide susceptibility, hazard and risk zoning for land use planning, Eng. Geol., 102, 85, 10.1016/j.enggeo.2008.03.022 Ferlisi, 2021, Quantitative analysis of the risk to road networks exposed to slow-moving landslides; a case study in the Campania region (southern Italy), Landslides, 18, 303, 10.1007/s10346-020-01482-8 Freund, 1997, 23 Ganaie, 2022, Ensemble deep learning: a review, Eng. Appl. Artif. Intel., 115, 10515 Goetz, 2015, Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling, Comput. Geosci., 81, 1, 10.1016/j.cageo.2015.04.007 Guo, 2021, Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management.Geosci, Front, 12, 101249 Guzzetti, 2012, Landslide inventory maps: new tools for an old problem, Earth Sci. Rev., 112, 42, 10.1016/j.earscirev.2012.02.001 Huang, 2020, Analysis and modeling of the combined effects of hydrological factors on a reservoir bank slope in the Three Gorges Reservoir area, China, Eng. Geol., 279, 10.1016/j.enggeo.2020.105858 Huang, 2017, Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine, Eng. Geol., 73, 209 Huang, 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, 919, 10.1080/19475705.2018.1482963 Huang, 2020, Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping, Catena, 191, 10.1016/j.catena.2020.104580 Huang, 2020, A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction, Landslides, 17, 217, 10.1007/s10346-019-01274-9 Huang, 2021, Uncertainty study of landslide susceptibility prediction considering the different attribute interval numbers of environmental factors and different data-based models., Catena, 202, 105250 Huang, 2022, Regional rainfall-induced landslide hazard warning based on landslide susceptibility mapping and a critical rainfall threshold, Geomorphology, 408, 10.1016/j.geomorph.2022.108236 Huang, 2022, Uncertainty pattern in landslide susceptibility prediction modelling: Effects of different landslide boundaries and spatial shape expressions, Geosci. Front., 13, 10.1016/j.gsf.2021.101317 Huang, 2022, Landslide susceptibility prediction using an incremental learning Bayesian Network model considering the continuously updated landslide inventories, Bull. Eng. Geol. Environ., 81, 250, 10.1007/s10064-022-02748-2 Huang, 2012, Analysis of waves generated by Gongjiafang landslide in Wu Gorge, three Gorges reservoir, on November 23, 2008, Landslides, 9, 395, 10.1007/s10346-012-0331-y Hungr, 2014, The Varnes classification of landslide types, an update, Landslides, 11, 167, 10.1007/s10346-013-0436-y Jin, 2022, Susceptibility analysis of land subsidence along the transmission line in the salt lake area based on remote sensing interpretation, Remote Sens, 14, 3229, 10.3390/rs14133229 Li, 2022, Stacking ensemble of deep learning methods for landslide susceptibility mapping in the Three Gorges Reservoir area, China, Stoch. Env. Res. Risk A., 36, 2207, 10.1007/s00477-021-02032-x Lin, 2021, Modelling the performance of EPB shield tunnelling using machine and deep learning algorithms, Geosci. Front., 12, 10.1016/j.gsf.2021.101177 Lin, 2022, Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network, J. Rock Mech. Geotech. Eng., 14, 1232, 10.1016/j.jrmge.2022.06.006 Lin, 2022, An extended TODIM-based model for evaluating risks of excavation system, Acta Geotech., 17, 1053, 10.1007/s11440-021-01294-7 Liu, 2021, Modelling of shallow landslides with machine learning algorithms, Geosci. Front., 12, 385, 10.1016/j.gsf.2020.04.014 Lyu, 2021, Calculation of groundwater head distribution with a close barrier during excavation dewatering in confined aquifer, Geosci. Front., 12, 791, 10.1016/j.gsf.2020.08.002 Mandal, 2021, Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India, Geosci. Front., 12, 101203, 10.1016/j.gsf.2021.101203 Meena, 2022, Landslide detection in the Himalayas using machine learning algorithms and U-Net, Landslides, 19, 1209, 10.1007/s10346-022-01861-3 Merghadi, 2020, Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance, Earth Sci. Rev., 207, 10.1016/j.earscirev.2020.103225 Miao, 2014, Mechanism of the slow-moving landslides in Jurassic red-strata in the Three Gorges Reservoir, China, Eng. Geol., 171, 59, 10.1016/j.enggeo.2013.12.017 Moore, 1991, Digital terrain modelling; a review of hydrological, geomorphological and biological applications, Hydrol. Process., 5, 3, 10.1002/hyp.3360050103 Nappo, 2019, Slow-moving landslides interacting with the road network: analysis of damage using ancillary data, in situ surveys and multi-source monitoring data, Eng. Geol., 260, 10.1016/j.enggeo.2019.105244 Ongsulee, 2017 Park, 2019, Landslide susceptibility mapping based on random forest and boosted regression tree models, and a comparison of their performance, Appl. Sci., 9, 942, 10.3390/app9050942 Peduto, 2017, Empirical fragility and vulnerability curves for buildings exposed to slow-moving landslides at medium and large scales, Landslides, 14, 1993, 10.1007/s10346-017-0826-7 Peduto, 2018, Quantitative analysis of consequences to masonry buildings interacting with slow-moving landslide mechanisms: a case study, Landslides, 15, 2017, 10.1007/s10346-018-1014-0 Peduto, 2021, Full integration of geomorphological, geotechnical, A-DInSAR and damage data for detailed geometric-kinematic features of a slow-moving landslide in urban area, Landslides, 18, 807, 10.1007/s10346-020-01541-0 Pham, 2017, A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS, Geomat. Nat. Haz. Risk, 8, 649, 10.1080/19475705.2016.1255667 Pham, 2017, Landslide susceptibility assessment using bagging ensemble based alternating decision trees, logistic regression and J48 decision trees methods: a comparative study, Geotech. Geol. Eng., 35, 2597, 10.1007/s10706-017-0264-2 Pham, 2018, A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling, Bull. Eng. Geol. Environ., 78, 2865, 10.1007/s10064-018-1281-y Pham, 2019, Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: hybrid machine learning approaches, Catena, 175, 203, 10.1016/j.catena.2018.12.018 Pham, 2020, Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping, Catena, 195, 104805, 10.1016/j.catena.2020.104805 Pham, 2021, Landslide susceptibility mapping using state-of-the-art machine learning ensembles, Geocarto Int., 1–26 Pokharel, 2021, Spatial clustering and modelling for landslide susceptibility mapping in the north of the Kathmandu Valley, Nepal, Landslides, 18, 1403, 10.1007/s10346-020-01558-5 Pradhan, 2010, Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling, Environ. Model. Softw., 25, 747, 10.1016/j.envsoft.2009.10.016 Reichenbach, 2018, A review of statistically-based landslide susceptibility models, Earth Sci. Rev., 180, 60, 10.1016/j.earscirev.2018.03.001 Sahin, 2020, Developing comprehensive geocomputation tools for landslide susceptibility mapping: LSM tool pack, Comput. Geosci., 144, 10.1016/j.cageo.2020.104592 Saito, 2009, Comparison of landslide susceptibility based on a decision tree model and actual landslide occurrence; the Akaishi Mountains, Japan, Geomorphol. (Amsterdam, Netherlands), 109, 108 Shen, 2021, Dynamic prediction of jet grouted column diameter in soft soil using Bi-LSTM deep learning, Acta Geotech., 16, 303, 10.1007/s11440-020-01005-8 Shen, 2022, Real-time prediction of shield moving trajectory during tunnelling, Acta Geotech., 17, 1533, 10.1007/s11440-022-01461-4 Shen, 2022, Enhancement of neural networks with an alternative activation function tanhLU, Expert Syst. Appl., 199, 10.1016/j.eswa.2022.117181 Thi Ngo, 2021, Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran, Geosci. Front., 12, 505, 10.1016/j.gsf.2020.06.013 Tien Bui, 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, 361, 10.1007/s10346-015-0557-6 Towfiqul Islam, 2021, Flood susceptibility modelling using advanced ensemble machine learning models, Geosci. Front., 12, 101075, 10.1016/j.gsf.2020.09.006 Ullah, 2022, Multi-hazard susceptibility mapping based on Convolutional Neural Networks, Geosci. Front., 13, 10.1016/j.gsf.2022.101425 Van Den Eeckhaut, 2006, Prediction of landslide susceptibility using rare events logistic regression: a case-study in the Flemish Ardennes (Belgium), Geomorphology, 76, 392, 10.1016/j.geomorph.2005.12.003 Vapnik, 1997, The nature of statistical learning theory, IEEE Trans. Neural Netw., 8, 1564, 10.1109/TNN.1997.641482 Wang, 2020, Comparative study of landslide susceptibility mapping with different recurrent neural networks, Comput. Geosci., 138, 10.1016/j.cageo.2020.104445 Wei, 2022, Application of tree-based ensemble models to landslide susceptibility mapping: a comparative study, Sustainability, 14, 6330, 10.3390/su14106330 Wu, 2020, Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping, Catena, 187, 104396, 10.1016/j.catena.2019.104396 Xiao, 2018, Numerical modeling of the June 24, 2015, Hongyanzi landslide generated impulse waves in Three Gorges Reservoir, China, Landslides, 15, 2385, 10.1007/s10346-018-1057-2 Xiao, 2020, Quantitative risk analysis of a rainfall-induced complex landslide in Wanzhou County, Three Gorges Reservoir, China, Int. J. Disaster Risk Sci., 11, 347, 10.1007/s13753-020-00257-y Yin, 2015, Numerical analysis on wave generated by the Qianjiangping landslide in Three Gorges Reservoir, China, Landslides, 12, 355, 10.1007/s10346-015-0564-7 Yin, 2020, Research on recently occurred reservoir-induced Kamenziwan rockslide in Three Gorges Reservoir, China, Landslides, 17, 1935, 10.1007/s10346-020-01394-7 Youssef, 2016, 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, 839, 10.1007/s10346-015-0614-1 Youssef, 2021, Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia, Geosci. Front., 12, 639, 10.1016/j.gsf.2020.05.010 Zeng, 2022, Landslide displacement prediction based on Variational mode decomposition and MIC-GWO-LSTM model, Stoch. Env. Res. Risk A Zeng, 2023, Quantitative risk assessment of the Shilongmen reservoir landslide in the Three Gorges area of China, Bulletin of Engineering Geology and the, 82, 214, 10.1007/s10064-023-03242-z Zeng, 2022, Groundwater level prediction based on a combined intelligence method for the Sifangbei landslide in the Three Gorges Reservoir Area, Sci. Rep. Zhang, 2021, Big data and machine learning in geoscience and geoengineering: introduction, Geosci. Front., 12, 327, 10.1016/j.gsf.2020.05.006 Zhang, 2021, Bagging-based machine learning algorithms for landslide susceptibility modeling, Nat. Hazards (Dordrecht), 110, 823, 10.1007/s11069-021-04986-1 Zhang, 2021, Application of deep learning algorithms in geotechnical engineering: a short critical review, Artif. Intell. Rev., 54, 5633, 10.1007/s10462-021-09967-1 Zhang, 2022, Stability analysis of the reservoir bank landslide with weak interlayer considering the influence of multiple factors, Geomat. Nat. Haz. Risk, 13, 2911, 10.1080/19475705.2022.2149356 Zhang, 2023, A new index for cutter life evaluation and ensemble model for prediction of cutter wear, Tunn. Undergr. Space Technol., 131, 10.1016/j.tust.2022.104830 Zhang, 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, 10.1016/j.cageo.2021.104966 Zhang, 2021, Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization, Geosci. Front., 12, 469, 10.1016/j.gsf.2020.03.007 Zhang, 2022, Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge, Gondw. Res., 109, 1, 10.1016/j.gr.2022.03.015 Zhang, 2022, Slope stability prediction using ensemble learning techniques: a case study in Yunyang County, Chongqing, China, J. Rock Mech. Geotech. Eng., 14, 1089, 10.1016/j.jrmge.2021.12.011 Zhang, 2023, An enhanced deep learning method for accurate and robust modelling of soil stress–strain response, Acta Geotech. Zheng, 2019, Investigation of Landslides that Occurred in August on the Chengdu-Kunming Railway, Sichuan, China, Geosciences, 9, 497, 10.3390/geosciences9120497 Zheng, 2021, Risk assessment of geohazards along Cheng-Kun railway using fuzzy AHP incorporated into GIS, Geomat. Nat. Haz. Risk, 12, 1508, 10.1080/19475705.2021.1933614 Zheng, 2022, Inundation risk assessment based on G-DEMATEL-AHP and its application to Zhengzhou flooding disaster., Sustain. Cities Soc., 86, 104138 Zhou, 2016, Application of time series analysis and PSO-SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, Eng. Geol., 204, 108, 10.1016/j.enggeo.2016.02.009 Zhou, 2018, Landslide susceptibility modeling applying machine learning methods: a case study from Longju in the Three Gorges Reservoir area, China, Comput. Geosci., 112, 23, 10.1016/j.cageo.2017.11.019 Zhou, 2022, Enhanced dynamic landslide hazard mapping using MT-InSAR method in the Three Gorges Reservoir Area, Landslides, 10.1007/s10346-021-01796-1