Show source-provided references
1. Landslide monitoring for risk mitigation by using corner reflector and satellite sar interferometry: The large landslide of Carlantino (Italy)
10.1016/j.catena.2016.12.006 ↗2. Statistical landslide susceptibility assessment of the mansehra and torghar districts, khyber pakhtunkhwa province, pakistan
10.1007/s11069-017-2992-2 ↗3. A nation-wide system for landslide mapping and risk management in italy: The second not-ordinary plan of environmental remote sensing
5. A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China)
10.1007/s12665-017-6981-2 ↗11. A new approach to temporal modelling for landslide hazard assessment using an extreme rainfall induced-landslide index
10.1016/j.enggeo.2016.10.006 ↗15. A comparative study of an expert knowledge-based model and two data-driven models for landslide susceptibility mapping
10.1016/j.catena.2018.04.003 ↗16. A comparative analysis of pixel-and object-based detection of landslides from very high-resolution images
17. GIS-based landslide susceptibility modelling: A comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models
10.1080/19475705.2017.1289250 ↗18. Landslide susceptibility mapping & prediction using support vector machine for Mandakini river basin, Garhwal Himalaya, India
10.1016/j.geomorph.2017.06.013 ↗19. Landslide susceptibility assessment in the hoa binh province of vietnam: A comparison of the levenberg-marquardt and bayesian regularized neural networks
20. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from kat landslides (Tokat-Turkey)
10.1016/j.cageo.2008.08.007 ↗21. Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using gis
10.1016/j.catena.2016.09.007 ↗22. Landslide Detection and Susceptibility Mapping Using Lidar and an Artificial Neural Network Approach: A Case Study in the Cuyahoga Valley National Park, Ohio
10.1007/s10346-015-0587-0 ↗23. Oh, H.-J., and Lee, S. (2017). Shallow landslide susceptibility modeling using the data mining models artificial neural network and boosted tree. Appl. Sci., 7.
10.3390/app7101000 ↗24. Pascale, S., Parisi, S., Mancini, A., Schiattarella, M., Conforti, M., Sole, A., Murgante, B., and Sdao, F. (2013). Landslide susceptibility mapping using artificial neural network in the urban area of senise and San Costantino Albanese (Basilicata, Southern Italy). Computational Science and Its Applications, Proceedings of the 13th International Conference on Computational Science and Its Applications, Ho Chi Minh City, Vietnam, 24–27 June 2013, Springer.
10.1007/978-3-642-39649-6_34 ↗25. Landslide susceptibility mapping at central Zab Basin, Iran: A comparison between analytical hierarchy process, frequency ratio and logistic regression models
10.1016/j.catena.2013.11.014 ↗27. Landslide susceptibility mapping using gis-based multi-criteria decision analysis, support vector machines, and logistic regression
10.1007/s10346-013-0391-7 ↗28. A comparative study of sequential minimal optimization-based support vector machines, vote feature intervals, and logistic regression in landslide susceptibility assessment using gis
10.1007/s12665-017-6689-3 ↗29. Comparison of four kernel functions used in support vector machines for landslide susceptibility mapping: A case study at Suichuan area (China)
10.1080/19475705.2016.1250112 ↗30. Pham, B.T., Jaafari, A., Prakash, I., and Bui, D.T. (2018). A novel hybrid intelligent model of support vector machines and the multiboost ensemble for landslide susceptibility modeling. Bull. Eng. Geol. Environ.
10.1007/s10064-018-1281-y ↗33. Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of least squares support vector machines and differential evolution optimization: A Case Study in Central Vietnam
10.1080/17538947.2016.1169561 ↗34. Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a novel hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization
10.1007/s10346-016-0711-9 ↗35. 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
10.1007/s10346-015-0614-1 ↗36. A tool for classification and regression using random forest methodology: Applications to landslide susceptibility mapping and soil thickness modeling
10.1007/s10666-016-9538-y ↗37. Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection, Greece
10.1007/s10346-015-0565-6 ↗39. Landslide susceptibility mapping using j48 decision tree with adaboost, bagging and rotation forest ensembles in the Guangchang Area (China)
10.1016/j.catena.2018.01.005 ↗40. Truong, X., Mitamura, M., Kono, Y., Raghavan, V., Yonezawa, G., Do, T., Tien Bui, D., and Lee, S. (2018). Enhancing prediction performance of landslide susceptibility model using hybrid machine learning approach of bagging ensemble and logistic model tree. Appl. Sci., 8.
10.3390/app8071046 ↗41. Dang, V.-H., Dieu, T.B., Tran, X.-L., and Hoang, N.-D. (2018). Enhancing the accuracy of rainfall-induced landslide prediction along mountain roads with a gis-based random forest classifier. Bull. Eng. Geol. Environ.
10.1007/s10064-018-1273-y ↗42. Trigila, A., Iadanza, C., Esposito, C., and Scarascia-Mugnozza, G. (2015). Comparison of logistic regression and random forests techniques for shallow landslide susceptibility assessment in Giampilieri (ne Sicily, Italy). Geomorphology.
10.1016/j.geomorph.2015.06.001 ↗43. Spatial prediction of rainfall-induced shallow landslides using gene expression programming integrated with gis: A case study in Vietnam
10.1007/s11069-018-3286-z ↗44. A novel fuzzy k-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using gis
10.1007/s10346-016-0708-4 ↗46. Assessment of landslide susceptibility for a landslide-prone area (North of Yenice, NW Turkey) by fuzzy approach
10.1007/s00254-001-0454-2 ↗47. Assessment of rainfall-generated shallow landslide/debris-flow susceptibility and runout using a gis-based approach: Application to Western Southern Alps of New Zealand
10.1007/s10346-014-0533-6 ↗48. Landslide susceptibility mapping at Hoa Binh Province (Vietnam) using an adaptive neuro-fuzzy inference system and gis
10.1016/j.cageo.2011.10.031 ↗49. Landslide susceptibility mapping by neuro-fuzzy approach in a landslide-prone area (Cameron Highlands, Malaysia)
10.1109/TGRS.2010.2050328 ↗50. Adaptive neuro-fuzzy inference system (anfis) modeling for landslide susceptibility assessment in a Mediterranean hilly area
10.1007/s10064-017-1125-1 ↗51. Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques
10.1016/j.geomorph.2017.09.007 ↗52. Landslide susceptibility assessment using a novel hybrid model of statistical bivariate methods (fr and woe) and adaptive neuro-fuzzy inference system (anfis) at Southern Zagros Mountains in Iran
10.1007/s12665-017-6558-0 ↗53. 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
10.1016/j.catena.2017.05.034 ↗54. 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
10.1007/s10346-015-0557-6 ↗55. Flood susceptibility mapping in dingnan county (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm
10.1016/j.jenvman.2019.06.102 ↗56. A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods
10.1016/j.jhydrol.2019.03.073 ↗57. Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles
10.1016/j.jhydrol.2019.05.089 ↗58. Tien Bui, D., Khosravi, K., Shahabi, H., Daggupati, P., Adamowski, J.F., M Melesse, A., Thai Pham, B., Pourghasemi, H.R., Mahmoudi, M., and Bahrami, S. (2019). Flood spatial modeling in Northern Iran using remote sensing and gis: A comparison between evidential belief functions and its ensemble with a multivariate logistic regression model. Remote Sens., 11.
10.3390/rs11131589 ↗59. Tien Bui, D., Shahabi, H., Shirzadi, A., Chapi, K., Hoang, N.-D., Pham, B., Bui, Q.-T., Tran, C.-T., Panahi, M., and Bin Ahamd, B. (2018). A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides. Remote Sens., 10.
10.3390/rs10101538 ↗60. Tien Bui, D., Khosravi, K., Li, S., Shahabi, H., Panahi, M., Singh, V., Chapi, K., Shirzadi, A., Panahi, S., and Chen, W. (2018). New hybrids of anfis with several optimization algorithms for flood susceptibility modeling. Water, 10.
10.3390/w10091210 ↗61. Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping
10.1016/j.jenvman.2018.03.089 ↗64. Alizadeh, M., Alizadeh, E., Asadollahpour Kotenaee, S., Shahabi, H., Beiranvand Pour, A., Panahi, M., Bin Ahmad, B., and Saro, L. (2018). Social vulnerability assessment using artificial neural network (ann) model for earthquake hazard in Tabriz City, Iran. Sustainability, 10.
10.3390/su10103376 ↗65. Lee, S., Panahi, M., Pourghasemi, H.R., Shahabi, H., Alizadeh, M., Shirzadi, A., Khosravi, K., Melesse, A.M., Yekrangnia, M., and Rezaie, F. (2019). Sevucas: A novel gis-based machine learning software for seismic vulnerability assessment. Appl. Sci., 9.
10.3390/app9173495 ↗66. Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability
10.1016/j.agrformet.2018.12.015 ↗67. Taheri, K., Shahabi, H., Chapi, K., Shirzadi, A., Gutiérrez, F., and Khosravi, K. (2019). Sinkhole susceptibility mapping: A comparison between bayes-based machine learning algorithms. Land Degrad. Dev.
10.1002/ldr.3255 ↗69. Modelling Gully-Erosion Susceptibility in a Semi-Arid Region, Iran: Investigation of Applicability of Certainty Factor and Maximum Entropy Models
10.1016/j.scitotenv.2018.11.235 ↗70. Tien Bui, D., Shirzadi, A., Shahabi, H., Chapi, K., Omidavr, E., Pham, B.T., Talebpour Asl, D., Khaledian, H., Pradhan, B., and Panahi, M. (2019). A novel ensemble artificial intelligence approach for gully erosion mapping in a semi-arid watershed (Iran). Sensors, 19.
10.3390/s19112444 ↗73. Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and uneec methods
10.1016/j.scitotenv.2019.06.320 ↗74. Chen, W., Pradhan, B., Li, S., Shahabi, H., Rizeei, H.M., Hou, E., and Wang, S. (2019). Novel hybrid integration approach of bagging-based fisher’s linear discriminant function for groundwater potential analysis. Nat. Resour. Res., 1–20.
10.1007/s11053-019-09465-w ↗75. Tien Bui, D., Shahabi, H., Shirzadi, A., Chapi, K., Alizadeh, M., Chen, W., Mohammadi, A., Ahmad, B., Panahi, M., and Hong, H. (2018). Landslide detection and susceptibility mapping by airsar data using support vector machine and index of entropy models in Cameron Highlands, Malaysia. Remote Sens., 10.
10.3390/rs10101527 ↗78. Spatial prediction of landslides using a hybrid machine learning approach based on random subspace and classification and regression trees
10.1016/j.geomorph.2017.12.008 ↗79. Landslide susceptibility modeling using reduced error pruning trees and different ensemble techniques: Hybrid machine learning approaches
10.1016/j.catena.2018.12.018 ↗81. A novel hybrid approach of landslide susceptibility modeling using rotation forest ensemble and different base classifiers
82. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using gis
10.1016/j.cageo.2012.08.023 ↗83. Landslide spatial modelling using novel bivariate statistical based naïve bayes, rbf classifier, and rbf network machine learning algorithms
10.1016/j.scitotenv.2019.01.329 ↗84. Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility
10.1016/j.catena.2018.12.033 ↗85. Landslide susceptibility assessment at the Wuning area, China: A comparison between multi-criteria decision making, bivariate statistical and machine learning methods
10.1007/s11069-018-3536-0 ↗87. Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree
10.1080/10106049.2019.1588393 ↗88. Nguyen, V.V., Pham, B.T., Vu, B.T., Prakash, I., Jha, S., Shahabi, H., Shirzadi, A., Ba, D.N., Kumar, R., and Chatterjee, J.M. (2019). Hybrid machine learning approaches for landslide susceptibility modeling. Forests, 10.
10.3390/f10020157 ↗89. Nguyen, P.T., Tuyen, T.T., Shirzadi, A., Pham, B.T., Shahabi, H., Omidvar, E., Amini, A., Entezami, H., Prakash, I., and Phong, T.V. (2019). Development of a novel hybrid intelligence approach for landslide spatial prediction. Appl. Sci., 9.
10.3390/app9142824 ↗90. Tien Bui, D., Shahabi, H., Omidvar, E., Shirzadi, A., Geertsema, M., Clague, J.J., Khosravi, K., Pradhan, B., Pham, B.T., and Chapi, K. (2019). Shallow landslide prediction using a novel hybrid functional machine learning algorithm. Remote Sens., 11.
10.3390/rs11080931 ↗91. Performance evaluation of the gis-based data mining techniques of best-first decision tree, random forest, and naïve bayes tree for landslide susceptibility modeling
10.1016/j.scitotenv.2018.06.389 ↗92. A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment
10.1080/10106049.2018.1425738 ↗93. Chen, W., Shahabi, H., Zhang, S., Khosravi, K., Shirzadi, A., Chapi, K., Pham, B., Zhang, T., Zhang, L., and Chai, H. (2018). Landslide susceptibility modeling based on gis and novel bagging-based kernel logistic regression. Appl. Sci., 8.
10.3390/app8122540 ↗94. Zhang, T., Han, L., Chen, W., and Shahabi, H. (2018). Hybrid integration approach of entropy with logistic regression and support vector machine for landslide susceptibility modeling. Entropy, 20.
10.3390/e20110884 ↗95. Abedini, M., Ghasemian, B., Shirzadi, A., Shahabi, H., Chapi, K., Pham, B.T., Bin Ahmad, B., and Tien Bui, D. (2018). A novel hybrid approach of bayesian logistic regression and its ensembles for landslide susceptibility assessment. Geocarto Int.
10.1080/10106049.2018.1499820 ↗96. Gis-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method
10.1016/j.catena.2018.01.012 ↗97. Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernel logistic regression classifier for landslide susceptibility modeling
10.1007/s10064-018-1401-8 ↗98. Shirzadi, A., Soliamani, K., Habibnejhad, M., Kavian, A., Chapi, K., Shahabi, H., Chen, W., Khosravi, K., Thai Pham, B., and Pradhan, B. (2018). Novel gis based machine learning algorithms for shallow landslide susceptibility mapping. Sensors, 18.
10.3390/s18113777 ↗99. A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve bayes tree classifiers for a landslide susceptibility assessment in Langao County, China
10.1080/19475705.2017.1401560 ↗