1 |
Mezaal, M.R. and B. Pradhan, 2018. An improved algorithm for identifying shallow and deep-seated landslides in dense tropical forest from airborne laser scanning data, Catena, 167: 147-159.
DOI
|
2 |
Moon, W. M., 1989. Integration of remote sensing and geological/geophysical data using Dempster-Shafer approach, Proc. of International Geoscience and Remote Sensing Symposium, Vancouver, BC, Jul. 10-14, vol. 2, pp. 838-841.
|
3 |
Moore, I.D., R.B. Grayson, and A.R. Ladson, 1991. Digital terrain modelling: a review of hydrological, geomorphological, and biological applications, Hydrological Processes, 5(1): 3-30.
DOI
|
4 |
Oh, H.-J., S. Lee, and S.-M. Hong, 2017. Landslide Susceptibility Assessment Using Frequency Ratio Technique with Iterative Random Sampling, Journal of Sensors, 2017: 21.
|
5 |
Oh, H.J. and B. Pradhan, 2011. Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area, Computers and Geosciences, 37(9): 1264-1276.
DOI
|
6 |
Park, N.W., 2011. Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis, Environmental Earth Sciences, 62(2): 367-376.
DOI
|
7 |
Pham, B.T., D.T. Bui, M.B. Dholakia, I. Prakash, H.V. Pham, K. Mehmood, and H.Q. Le, 2017. A novel ensemble classifier of rotation forest and Naive Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS, Geomatics, Natural Hazards and Risk, 8(2): 649-671.
DOI
|
8 |
Pham, B.T., I. Prakash, S.K. Singh, A. Shirzadi, H. Shahabi, T.T.T. Tran, and D.T. Bui, 2019. Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches, Catena, 175: 203-218.
DOI
|
9 |
Pourghasemi, H.R. and O. Rahmati, 2018. Prediction of the landslide susceptibility: Which algorithm, which precision?, Catena, 162: 177-192.
DOI
|
10 |
Pourghasemi, H.R., B. Pradhan, and C. Gokceoglu, 2012. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran, Natural Hazards, 63(2): 965-996.
DOI
|
11 |
Pradhan, B. and S. Lee, 2010. Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling, Environmental Modelling and Software, 25(6): 747-759.
DOI
|
12 |
Pradhan, B., 2013. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS, Computers and Geosciences, 51: 350-365.
DOI
|
13 |
Razavizadeh, S., K. Solaimani, M. Massironi, and A. Kavian, 2017. Mapping landslide susceptibility with frequency ratio, statistical index, and weights of evidence models: a case study in northern Iran, Environmental Earth Sciences, 76(14): 499.
DOI
|
14 |
Althuwaynee, O.F., B. Pradhan, H.J. Park, and J.H. Lee, 2014b. A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping, Landslides, 11(6): 1063-1078.
DOI
|
15 |
Shafer, G., 1976. A Mathematical Theory of Evidence, Princeton University Press, Princeton, London.
|
16 |
Tsangaratos, P., I. Ilia, H. Hong, W. Chen, and C. Xu, 2017. Applying Information Theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China, Landslides, 14(3): 1091-1111.
DOI
|
17 |
Wright, D.F. and G.F. Bonham-Carter, 1996. VHMS favourability mapping with GIS-based integration models, Chisel Lake-Anderson Lake area, In: EXTECH I, a multidisciplinary approach to massive sulphide research in the Rusty Lake-Snow Lake Greenstone Belts, Manitoba, Natural Resources Canada, Ottawa, Canada, p. 402.
|
18 |
Zhu, A.X., Y. Miao, L. Yang, S. Bai, J. Liu, and H. Hong, 2018. Comparison of the presence-only method and presence-absence method in landslide susceptibility mapping, Catena, 171: 222-233.
DOI
|
19 |
Song, K.Y., H.J. Oh, J. Choi, I. Park, C. Lee, and S. Lee, 2012. Prediction of landslides using ASTER imagery and data mining models, Advances in Space Research, 49(5): 978-993.
DOI
|
20 |
Althuwaynee, O.F., B. Pradhan, H.J. Park, and J.H. Lee, 2014a. A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping, Catena, 114: 21-36.
DOI
|
21 |
An, P., W. M. Moon, and G. F. Bonham-Carter, 1992. On knowledge based approach to integrating remote sensing, geophysical and geological information, Proc. of International Geoscience and Remote Sensing Symposium, Center NASA, Clear Lake Area Houston, May 26-29, vol. 1, pp. 34-38.
|
22 |
Anbalagan, R., 1992. Landslide susceptibility evaluation and zonation mapping in mountainous terrain, Engineering Geology, 32(4): 269-277.
DOI
|
23 |
Bonham-Carter, G. F., 1994. Geographic Information Systems for geoscientists, modeling with GIS, Pergamon Press, Oxford, UK.
|
24 |
Bonham-Carter, G.F., F.P. Agterberg, and D.F. Wright, 1988. Integration of geological datasets for gold exploration in Nova Scotia, Photogrammetic Engineering and Remote Sensing, 54(11): 1585-1592.
|
25 |
Bui, D.T., B. Pradhan, I. Revhaug, D.B. Nguyen, H.V. Pham, and Q.N. Bui, 2015. A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam), Geomatics, Natural Hazards and Risk, 6(3): 243-271.
DOI
|
26 |
Chen, W., H.R. Pourghasemi, and Z. Zhao, 2017c. A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping, Geocarto International, 32(4): 367-385.
DOI
|
27 |
Carranza, E.J.M., T. Woldai, and E.M. Chikambwe, 2005. Application of data-driven evidential belief functions to prospectivity mapping for aquamarine-bearing pegmatites, Lundazi District, Zambia, Natural Resources Research, 14(1): 47-63.
DOI
|
28 |
Chauhan, S., M. Sharma, and M.K. Arora, 2010. Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model, Landslides, 7(4): 411-423.
DOI
|
29 |
Chen, W., H. Chai, Z. Zhao, Q. Wang, and H. Hong, 2016. Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China, Environmental Earth Sciences, 75(6): 474.
DOI
|
30 |
Chen, W., H. Shahabi, A. Shirzadi, T. Li, C. Guo, H. Hong, W. Li, D. Pan, J. Hui, M. Ma, M. Xi, and B. Bin Ahmad, 2018a. A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment, Geocarto International, 33(12): 1398-1420.
DOI
|
31 |
Chen, W., H.R. Pourghasemi, M. Panahi, A. Kornejady, J. Wang, X. Xie, and S. Cao, 2017b. Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques, Geomorphology, 297: 69-85.
DOI
|
32 |
Chen, W., M. Panahi, and H.R. Pourghasemi, 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.
DOI
|
33 |
Dhakal, A.S. and R.C. Sidle, 2003. Long-term modeling of landslides for different forest management practices, Earth Surface Processes and Landforms, 28(8): 853-868.
DOI
|
34 |
Chen, W., X. Xie, J. Peng, H. Shahabi, H. Hong, D.T. Bui, Z. Duan, S. Li, and A.X. Zhu, 2018b. GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method, Catena, 164: 135-149.
DOI
|
35 |
Edeso, J.M., A. Merino, M.J. Gonzalez, and P. Marauri, 1999. Soil erosion under different harvesting managements in steep forest lands from northern Spain, Land Degradation & Development, 10(1): 79-88.
DOI
|
36 |
Choi, J., H.J. Oh, H.J. Lee, C. Lee, and S. Lee, 2012. Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS, Engineering Geology, 124(1): 12-23.
DOI
|
37 |
Chu, L., L.J. Wang, J. Jiang, X. Liu, K. Sawada, and J. Zhang, 2019. Comparison of landslide susceptibility maps using random forest and multivariate adaptive regression spline models in combination with catchment map units, Geosciences Journal, 23(2): 341-355.
DOI
|
38 |
Conoscenti, C., E. Rotigliano, M. Cama, N.A. Caraballo-Arias, L. Lombardo, and V. Agnesi, 2016. Exploring the effect of absence selection on landslide susceptibility models: A case study in Sicily, Italy, Geomorphology, 261: 222-235.
DOI
|
39 |
Dempster, A.P., 1967. Upper and lower probabilities induced by a multivalued mapping, Annals of Mathematical Statistics, 38(2): 325-339.
DOI
|
40 |
Dempster, A.P., 1968. A generalization of Bayesian inference, Journal of the Royal Statistical Society Statistical Methodology Series B, 30(2): 205-247.
|
41 |
Dou, J., H. Yamagishi, H.R. Pourghasemi, A.P. Yunus, X. Song, Y. Xu, and Z. Zhu, 2015. An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan, Natural Hazards, 78(3): 1749-1776.
DOI
|
42 |
Florinsky, I. V., 2012. Digital Terrain Analysis in Soil Science and Geology, Elsevier Press, Amsterdam, Netherland.
|
43 |
Fatemi Aghda, S.M., V. Bagheri, and M. Razifard, 2018. Landslide Susceptibility Mapping Using Fuzzy Logic System and Its Influences on Mainlines in Lashgarak Region, Tehran, Iran, Geotechnical and Geological Engineering, 36(2): 915-937.
|
44 |
Feizizadeh, B., T. Blaschke, D. Tiede, and M.H.R. Moghaddam, 2017. Evaluating fuzzy operators of an object-based image analysis for detecting landslides and their changes, Geomorphology, 293: 240-254.
DOI
|
45 |
Felicisimo, A.M., A. Cuartero, J. Remondo, and E. Quiros, 2013. Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: A comparative study, Landslides, 10(2): 175-189.
DOI
|
46 |
Hines, J. W., 1997. Fuzzy and neural approaches in engineering, Wiley Press, New York, USA.
|
47 |
Hong, H., H.R. Pourghasemi, and Z.S. Pourtaghi, 2016. Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models, Geomorphology, 259: 105-118.
DOI
|
48 |
Hong, H., J. Liu, D.T. Bui, B. Pradhan, T.D. Acharya, B.T. Pham, A.X. Zhu, W. Chen, and B.B. Ahmad, 2018. Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China), Catena, 163: 399-413.
DOI
|
49 |
Jebur, M.N., B. Pradhan, and M.S. Tehrany, 2014. Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale, Remote Sensing of Environment, 152: 150-165.
DOI
|
50 |
Jaafari, A., M. Panahi, B.T. Pham, H. Shahabi, D.T. Bui, F. Rezaie, and S. Lee, 2019. Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility, Catena, 175: 430-445.
DOI
|
51 |
Kaab, A., 2002. Monitoring high-mountain terrain deformation from repeated air- and spaceborne optical data: examples using digital aerial imagery and ASTER data, ISPRS Journal Photogrammetry and Remote Sensing, 57(1-2): 39-52.
DOI
|
52 |
Kalantar, B., B. Pradhan, S. Amir Naghibi, A. Motevalli, and S. Mansor, 2018. Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN), Geomatics, Natural Hazards and Risk, 9(1): 49-69.
DOI
|
53 |
Kincal, C., A. Akgun, and M.Y. Koca, 2009. Landslide susceptibility assessment in the Izmir (West Anatolia, Turkey) city center and its near vicinity by the logistic regression method, Environmental Earth Sciences, 59(4): 745-756.
DOI
|
54 |
Lee, J.H., M.I. Sameen, B. Pradhan, and H.J. Park, 2018. Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods, Geomorphology, 303: 284-298.
DOI
|
55 |
Lee, M.J., I. Park, and S. Lee, 2015a. 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, Environmental Earth Sciences, 74(1): 413-429.
DOI
|
56 |
Lee, S., 2007. Application and verification of fuzzy algebraic operators to landslide susceptibility mapping, Environmental Geology, 52(4): 615-623.
DOI
|
57 |
Lee, S. and B. Pradhan, 2006. Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia, Journal of Earth System Science, 115(6): 661-672.
DOI
|
58 |
Lee, S. and T. Sambath, 2006. Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models, Environmental Geology, 50(6): 847-855.
DOI
|
59 |
Lee, S., 2005. Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data, International Journal of Remote Sensing, 26(7): 1477-1491.
DOI
|
60 |
Lee, S., 2013. Landslide detection and susceptibility mapping in the Sagimakri area, Korea using KOMPSAT-1 and weight of evidence technique, Environmental Earth Sciences, 70(7): 3197-3215.
DOI
|
61 |
Lee, S., 2019. Current and Future Status of GIS-based Landslide Susceptibility Mapping: A Literature Review, Korean Journal of Remote Sensing, 35(1): 179-193.
DOI
|
62 |
Lee, S., J. Hwang, and I. Park, 2013. Application of data-driven evidential belief functions to landslide susceptibility mapping in Jinbu, Korea, Catena, 100: 15-30.
DOI
|
63 |
Pham, B.T., D. Tien Bui, I. Prakash, and M.B. Dholakia, 2016. Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS, Natural Hazards, 83(1): 97-127.
|
64 |
Lee, S., J.S. Won, S.W. Jeon, I. Park, and M.J. Lee, 2015b. Spatial Landslide Hazard Prediction Using Rainfall Probability and a Logistic Regression Model, Mathematical Geosciences, 47(5): 565-589.
DOI
|
65 |
Lee, M.J., J.W. Choi, H.J. Oh, J.S. Won, I. Park, and S. Lee, 2012a. Ensemble-based landslide susceptibility maps in Jinbu area, Korea, Environmental Earth Sciences, 67(1): 23-37.
DOI
|
66 |
Lee, S., K.Y. Song, H.J. Oh, and J. Choi, 2012b. Detection of landslides using web-based aerial photographs and landslide susceptibility mapping using geospatial analysis, International Journal of Remote Sensing, 33(16): 4937-4966.
DOI
|
67 |
Li, L., H. Lan, C. Guo, Y. Zhang, Q. Li, and Y. Wu, 2017. A modified frequency ratio method for landslide susceptibility assessment, Landslides, 14(2): 727-741.
DOI
|
68 |
Martha, T.R., N. Kerle, V. Jetten, C.J. Van Westen, and K. Vinod Kumar, 2010. Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods, Geomorphology, 116(1-2): 24-36.
DOI
|