參考文獻 |
Abdel-Rahman, E.M., Mutanga, O., Adam, E., Ismail, R., 2014. Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers. ISPRS Journal of Photogrammetry and Remote Sensing, 88, 45-59.
Aksoy, B., Ercanoglu, M., 2012. Landslide identification and classification by object-based image analysis and fuzzy logic: an example from the Azdavay region (Kastamonu, Turkey). Computers & Geosciences, 38: 87-98.
Ahmed, M.F., Rogers, J.D., 2015. Regional level landslide inventory maps of the Shyok River watershed, Northern Pakistan. Bulletin of Engineering Geology and the Environment, 75(2): 563-574.
Ardizzone, F., Cardinali, M., Carrara, A., Guzzetti, F., Reichenbach, P., 2002. Impact of mapping error on the reliability of landslide hazard maps. Natural Hazards and Earth System Sciences, 2: 3-14.
Bai, S., Lu, G., Wang, J., Zhou, P., Ding L., 2011. GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang, China. Environmental Earth Sciences, 62: 139-149.
Belgiu, M., Dragut, L., 2016. Random forest in remote sensing: a review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114: 24-31.
Berry, M.J., Linoff, G.S., 2000. Mastering data mining: the art and science of customer relationship management. 197 p., Wiley, New York, USA.
Brabb, E.E., 1984. Innovative approaches to landslide hazard mapping. The 4th International Symposium on Landslides, 16-21 September 1984, Toronto, Canada.
Brabb, E.E., 1991. The world landslide problem. Episodes, 14: 52-61.
Brabb, E.E., Pampeyan, E.H., Bonilla, M.G., 1978. Landslide susceptibility in San Mateo County, California. US Geological Survey Miscellaneous Field Studies Map, MF-360, Map at 1: 62,500 scale.
Breiman, L., 2001. Random forests. Machine Learning, 45: 5-32.
Brenning, A., 2005. Spatial prediction models for landslide hazards: review, comparison and evaluation. Natural Hazards and Earth System Sciences, 5: 853-862.
Bui, D.T., Pradhan, B., Lofman, O., Revhaug, I., Dick, O.B., 2012. Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena, 96: 28-40.
Carrara, A., 1983. Multivariate models for landslide hazard evaluation. Mathematical Geology, 15(3): 403-426.
Carrara, A., Cardinali, M., Detti, R., Guzzetti, F., Pasqui, V., Reichenbach, P., 1991. GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Processes and Landforms, 16: 427-445.
Carrara, A., Cardinali, M., Guzzetti, F., 1992. Uncertainty in assessing landslide hazard and risk. ITC Journal, 2: 172-183.
Carrara A., Carratelli, E.P., Merenda, L., 1977. Computer-based data bank and statistical analysis of slope instability phenomena. Zeitschrift für Geomorphologie, 21: 187-222.
Chan, J.C., Beckers, P., Spanhove, T., Borre, J.V., 2012. An evaluation of ensemble classifiers for mapping Natura 2000 heathland in Belgium using spaceborne angular hyperspectral (CHRIS/Proba) imagery. International Journal of Applied Earth Observation and Geoinformation, 18: 13-22.
Chang, C.P., Angelier, J., Huang, C.Y., Liu, C.S., 2001. Structural evolution and significance of a mélange in a collision belt: the Lichi Melange and the Taiwan arc-continent collision. Geological Magazine, 138(6): 633-651.
Chang, K.-T., Chiang, S.-H., Chen, Y.-C., Mondini, A.C., 2014. Modeling the spatial occurrence of shallow landslides triggered by typhoons. Geomorphology, 208: 137-148.
Chang, K.-T., Chiang, S.-H., Hsu, M.-L., 2007. Modeling typhoon- and earthquake-induced landslides in a mountainous watershed using logistic regression. Geomorphology, 89: 335-347.
Chang, S.-F., 2013. Using slope-unit for landslide susceptibility assessment. Master Thesis, Graduate Institute of Applied Geology, National Central University (in Chinese with English abstract).
Chang, Y.-C., 2012. Landslide potential analysis: a case study in the Lao-Nong River watershed. Master Thesis, Department of Urban Planning and Spatial Information, Feng Chia University (in Chinese with English abstract).
Chen, C.-H., Tan, C.-H., Chi, S.-Y., Su, T.-W., 2011. An application of GIS-based deterministic model for assessment of regional rainfall-induced landslide potential-example of Kao-Ping river watershed. Journal of Chinese Soil and Water Conservation, 42(1): 1-11 (in Chinese with English abstract).
Chen, W.W., Shen, J.-P., Wang, J.-A., Tsai, F., 2015a. Scripting STABL with PSO for analysis of slope stability. Neurocomputing, 148: 167-174.
Chen, X., Vierling, L., Deering, D., 2005. A simple and effective radiometric correction method to improve landscape change detection across sensors and across time. Remote Sensing of Environment, 98: 63-79.
Chen, Y.-C., Chang, K.-T., Lee, H.-Y., Chiang, S.-H., 2015b. Average landslide erosion rate at the watershed scale in southern Taiwan estimated from magnitude and frequency of rainfall. Geomorphology, 228: 756-764.
Chiang, S.-H., Chang, K.-T., Mondini, A.C., Tsai, B.-W., Chen, C.-Y., 2012. Simulation of event-based landslides and debris flows at watershed level. Geomorphology, 138: 306-318.
Chu, C.-M., Tsai, B.-W., Chang, K.-T., 2009. Integrating decision tree and spatial cluster analysis for landslide susceptibility zonation. World Academy of Science, Engineering and Technology, 35: 479-483.
Chung, Y.-C., 2009. Probe into the regional landslide susceptibility analysis- a case study in the Shimen reservoir catchment Area. Master Thesis, Graduate Institute of Geophysics, National Central University (in Chinese with English abstract).
Choi, J., Oh, H.-J., Lee, H.-J., Lee, C., Lee, S., 2012. Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Engineering Geology, 124: 12-23.
Choi, J., Oh, H.-J., Won, J.-S., and Lee, S., 2010. Validation of an artificial neural networks model for landslide susceptibility mapping. Environmental Earth Sciences, 60: 473-483.
Claessens, L., Heuvelink, G.B.M., Schoorl, J.M., Veldkamp, A., 2005. DEM resolution effects on shallow landslide hazard and soil redistribution modeling. Earth Surface Processes and Landforms, 30: 461-477.
Clerici, A., Perego, S., Tellini, C., Vescovi, P., 2006. A GIS-based automated procedure for landslide susceptibility mapping by the condition analysis method: the Baganza valley case study (Italian Northern Apennines). Environmental Geology, 50: 941-961.
Cruden, D.M., Varnes, D.J., 1996. Landslide types and processes. In: Turner, A.K., Schuster, R.L. (Eds.), Landslides Investigation and Mitigation. Special Report 247, Transportation Research Board, National Research Council. National Academy Press, Washington, DC, pp. 36-75.
Dahal, R.K., Hasegawa, S., Nonomura, A., Yamanaka, M., Dhakal, S., Poudyal, P., 2008. Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence. Geomorphology, 102: 496-510.
Dai, F.C., Lee, C. F., 2002. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology, 42: 213-228.
Dai, F.C., Lee, C.F., and Ngai, Y.Y., 2002. Landslide risk assessment and management: an overview. Engineering Geology, 64: 65-87.
Dai, F.C., Lee, C.F., 2003. A spatialtemporal probabilistic modeling of storm-induced shallow landslide using aerial photographs and Logistic regression. Earth Surface Processes and Landforms, 28: 527-545.
Das, B.M., 2000. Fundamentals of geotechnical engineering. Brooks/Cole, CA, U.S., 593 p.
Deng, Y. C., Tsai, F., Hwang, J.H., 2016. Landslide characteristics in the area of Xiaolin Village during Morakot typhoon. Arabian Journal of Geosciences, 9(5), 16 pages.
Desai, A., Jadav, P.M., 2012. An empirical evaluation of adaboost extensions for cost-sensitive classification. International Journal of Computer Applications, 44(13): 34-41.
Dietrich, E.W., Reiss, R., Hsu, M.L., Montgomery, D.R., 1995. A process-based model for colluvial soil depth and shallow landsliding using digital elevation data. Hydrological Processes, 9: 383-400.
Dilley, M., Chen, R., Deichmann, U., Lerner-Lam, A.L., Margaret, A., 2005. Natural disaster hotspots: a global risk analysis. Technical Report, The World Bank.
Donati, L., Turrini, M.C., 2002. An objective method to rank the importance of the factors predisposing to landslide with the GIS methodology: application to an area of the Apennines (Valnerina; Perugia, Italy). Engineering Geology, 63(3-4): 277-289.
Dou, J., Chang, K.-T., Chen, S., Yunus, A.P., Liu, J.-K., Xia, H., Zhu, Z., 2015a. Automatic case-based reasoning approach for landslide detection: integration of object-oriented image analysis and a genetic algorithm. Remote Sensing, 7: 4318-4342.
Dou, J., Paudel, U., Oguchi, T., Uchiyama, S., Hayakawa, Y.S., 2015b. Shallow and deep-seated landslide differentiation using support vector machines: a case study of the Chuetsu Area, Japan. Terrestrial, Atmospheric and Oceanic Sciences, 26(2): 227-239.
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 Journal of Photogrammetry and Remote Sensing, 105: 38-53.
Duncan, J.M., Wright, S.G., 1980. The accuracy of equilibrium methods of slope stability analysis. Engineering Geology, 16: 5-17.
Elith, J., Burgman, M.A., Regan, H.M., 2002. Mapping epistemic uncertainties and vague concepts in predictions of species distribution. Ecological Modelling, 157: 313-329.
Elkan, C., 2001. The foundations of cost-sensitive learning. Proc. of the Seventeenth International Joint Conference on Artificial Intelligence, 4-10 Aug., Seattle, USA.
Ercanoglu, M., Gokceoglu, C., 2004. Use of the fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Engineering Geology, 175: 229-250.
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., 1996. The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11): 27-34.
Fell, R., 1994. Landslide risk assessment and acceptable risk. Canadian Geotechnical Journal, 31: 261-272.
Fell, R., Hartford, D., 1997. Landslide risk management. In: Cruden, D., Fell, R. (Eds.), Landslide Risk Assessment. Balkema, Rotterdam, pp. 51-109.
Feng, F.-L., Kao, J.-T., 1999. Applying Kriging interpolation model in precipitation properties mapping. NTU Experimental Forest Report, 13(2): 155-163 (in Chinese with English abstract).
Galli, M., Ardizzone, F., Cardinali, M., Guzzetti, F., Reichenbach, P., 2008. Comparing landslide inventory maps. Geomorphology, 94: 268-289.
Gariano, S.L., Brunetti, M.T., Iovine, G., Melillo, M., Peruccacci, S., Terranova, O., Vennari, C., Guzzetti, F., 2015. Calibration and validation of rainfall thresholds for shallow landslide forecasting in Sicily, Southern Italy. Geomorphology, 228: 653-665.
Gemitzi, A., Falalakis, G., Eskioglou, P., Petalas, C., 2011. Evaluating landslide susceptibility using environmental factors, fuzzy membership functions and GIS. Global NEST, 13(1): 28-40.
Goetz, J.N., Brenning, A., Petschko, H., Leopold, P., 2015. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Computers & Geosciences, 81: 1-11.
Goetz, J.N., Guthrie, R.H., Brenning, A., 2011. Integrating physical and empirical landslide susceptibility models using generalized additive models. Geomorphology, 129: 376-386.
Gorsevski, P.V., Jankowski, P., 2008. Discerning landslide susceptibility using rough sets. Computer, Environment and Urban Systems, 32: 53-65.
Gorsevski, P.V., and Jankowski, P., 2010. An optimized solution of multi-criteria evaluation analysis of landslide susceptibility using fuzzy sets and Kalman filter. Computers & Geosciences, 36: 1005-1020.
Gu, B.-H., Hsiao, C.-Y., Lin, B.-S., Leung, W.-Y., Cheng, C.-T., 2009. To establish the predicting model of shallow landslide volume induced by heavy rainfall in Shin-Men Reservoir. In: Proc. of 13th Conference on Current Researches in Geotechnical Engineering in Taiwan, 26-28 Aug. 2009, I-Lan, Taiwan (in Chinese with English abstract).
Guo, L., Chehata, N., Mallet, C., Boukir, S., 2011. Relevance of airborne lidar and multispectral image data for urban scene classification using random forests. ISPRS Journal of Photogrammetry and Remote Sensing, 66(1): 56-66.
Guzzetti, F., 2005. Landslide hazard and risk assessment. PhD Dissertation, Department of Geography, University of Bonn, Germany.
Guzzetti, F., Carrara, A., Cardinali, M., Reichenbach, P., 1999. Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 31: 181-216.
Guzzetti, F., Galli, M., Reichenbach, P., Ardizzone, F., Cardinali, M., 2006a. Landslide hazard assessment in the Collazzone area, Umbria, Central Italy. Natural Hazards and Earth System Sciences, 6: 115-131.
Guzzetti, F., Mondini, A.C., Cardinali, M., Fiorucci, F., Santangelo, M., Chang, K.T., 2012. Landslide inventory maps: new tools for an old problem. Earth-Science Reviews, 112: 42-66.
Guzzetti, F., Reichenbach, P., Ardizzone, F., Cardinali, M., and Galli, M., 2006b. Estimating the quality of landslide susceptibility models. Geomorphology, 81: 166-184.
Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., Ardizzone, F., 2005. Probabilistic landslide hazard assessment at the basin scale. Geomorphology, 72: 272-299.
Harp, E.L., Keefer, D.K., Sato, H.P., Yagi, H., 2011. Landslide inventories: the essential part of seismic landslide hazard analyses. Engineering Geology, 122: 9-21.
Heckmann, T., Gegg, K., Gegg, A., Becht, M., 2014. Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows. Natural Hazards and Earth System Sciences, 14(2): 259-278.
Highland, L.M., Bobrowsky, P., 2008. The landslide handbook¬¬¬- a guide to understanding landslides. U.S. Geological Survey Circular 1325, 129 p.
Huang, J.-B., 2008. Application of geographical information systems and Logistic Regression on landslide potential assessment. Master Thesis, Department of Water Resources Engineering and Conservation, Feng Chia University (in Chinese with English abstract).
Huang, J.-C., Kao, S.-J., Hsu, M.-L., Lin, J.-C., 2006. Stochastic procedure to extract and to integrate landslide susceptibility maps: an example of mountainous watershed in Taiwan. Natural Hazards and Earth System Sciences, 6: 803-815.
Hungr, O., Leroueil, S., Picarelli, L., 2014. The Varnes classification of landslide types, an update. Landslide, 11(2): 167-194.
Hussin, H.Y., Zumpano, V., Reichenbach, P., Sterlacchini, S., Micu, M., van Westen, C., Balteanu, B., 2016. Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model. Geomorphology, 253: 508-523.
Iovine, G.G.R., Greco, R., Gariano, S.L., Pellegrino, A.D., Terranova, O.G., 2014. Shallow-landslide susceptibility in the Costa Viola mountain ridge (southern Calabria, Italy) with considerations on the role of causal factors. Natural Hazards, 73(1): 111-136.
Kanungo, D.P., Arora, M.K., Sarkar, S., Gupta, R.P., 2006. A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering Geology, 85: 347-366.
Kavzoglu, T., Sahin, E.K., Colkesen, I., 2015. Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm. Engineering Geology, 192: 101-112.
Lainas, S., Sabatakakis, N., Koukis, G., 2016. Rainfall thresholds for possible landslide initiation in wildfire-affected area of western Greece. Bulletin of Engineering Geology and the Environment, 75(3): 883-896.
Lee, C.-T., 2008. GIS Application in Landslide Hazard Analysis. Pacific Neighborhood Consortium, Annual Conference 2008.
Lee, C.-T., 2009. Review and prospect on landslide and debris flow hazard analysis. Journal of the Taiwan Society of Public Works, 5(1): 1-29 (in Chinese with English abstract).
Lee, C.-T., Huang, C.-C., Lee, J.-F., Pan, K.-L., Lin, M.-L., Dong, J.J., 2008. Statistical approach to storm event-induced landslide susceptibility. Natural Hazards and Earth System Sciences, 8: 941-960.
Lee, S., 2007. Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Environmental Geology, 52: 615-623.
Lee, S., Choi, J., Woo, I., 2004. The effect of spatial resolution on the accuracy of landslide susceptibility mapping: a case study in Boun, Korea. Geosciences Journal, 8(1): 51-60.
Lee, S., Lee, M.-J., 2006. Detecting landslide location using KOMPSAT 1 and its application to landslide-susceptibility mapping at the Gangneung area, Korea. Advances in Space Research, 38: 2261-2271.
Lee, S., Ryu, J.-H., Lee, M.-J., Won, J.-S., 2006. The application of artificial neural networks to landslide susceptibility mapping at Janghung Korea. Mathematical Geology, 38(2): 199-220.
Li, H.-W., 2010. Coupling TRIGRS and TOPMODEL in shallow landslide prediction. Master Thesis, Graduate Institute of Applied Geology, National Central University (in Chinese with English abstract).
Lin, P.-W., 2010. The effect of cumulative horizontal seismic acceleration on landslide susceptibility with logistic regression model. Master Thesis, Department of Bioenvironmental Systems Engineering, National Taiwan University (in Chinese with English abstract).
Lin, W.-C., 2012. Application of satellite image classification technology in landslide potential analysis and construction of risk assessment model. Master Thesis, Land Management and Development Department, Chang Jung Christian University (in Chinese with English abstract).
Lin, Y.H., 2003. Application of neural networks to landslide susceptibility analysis. Master Thesis, Institute of Applied Geology, National Central University, 91 p (in Chinese with English abstract).
Lu, P., Bai, S., Casagli, N., 2015. Spatial relationships between landslide occurrences and land cover across the Arno river basin (Italy). Environmental Earth Sciences, 74(7): 5541-5555.
Malamud, B.D., Turcotte, D.L., Guzzetti, F., Reichenbach, P., 2004. Landslide inventories and their statistical properties. Earth Surface Processes and Landforms, 29: 687-711.
Melchiorre, C., Matteucci, M., Azzoni, A., Zanchi, A., 2008. Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology, 94: 379-400.
Mellor, A., Boukir, S., Haywood, A., Jones, S., 2015. Exploring issues of training data imbalance and mislabeling on random forest performance for large area land cover classification using the ensemble margin. ISPRS Journal of Photogrammetry and Remote Sensing, 105: 155-168.
Mettenicht, G., Hurni, L., Gogu, R., 2005. Remote sensing of landslides: an analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments. Remote Sensing of Environment, 98: 284-303.
Miller, H.J., Han, J., 2001. Geographic data mining and knowledge discovery, pp.3-32. London: New York : Taylor & Francis.
Mondini, A.C., Chang, K.-T., 2014. Combing spectral and geoenvironmental information for probabilistic event landslide mapping. Geomorphology, 213: 183-189.
Mondini, A.C., Chang, K.-T., Yin, H.-Y., 2011a. Combing multiple change detection indices for mapping landslides triggered by typhoons. Geomorphology, 134: 440-451.
Mondini, A.C., Guzzetti, F., Reichenbach, P., Rossi, M., Cardinali, M., Ardizzone, F., 2011b. Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images. Remote Sensing of Environment, 115: 1743-1757.
Mondini, A.C., Marchesini, I., Rossi, M., Chang, K.-T., Pasquariello, G., Guzzetti, F., 2013. Bayesian framework for mapping and classifying shallow landslides exploiting remote sensing and topographic data. Geomorphology, 201: 135-147.
Mondini, A.C., Viero, A., Cavalli, M., Marchi, L., Herrera, G., Guzzetti, F., 2014. Comparison of event landslide inventories: the Pogliaschina catchment test case, Italy. Natural Hazards and Earth System Sciences, 14: 1749-1759.
Mosleh, A., 1986. Hidden sources of uncertainty: judgment in the collection and analysis of data. Nuclear Engineering and Design, 93: 187-198.
NDPPC, 2009. Disaster response disposition report of Typhoon Morakot. No. 74, Technical Report, National Disaster Prevention and Protection Commission, Taiwan, available at: http://www.nfa.gov.tw/uploads/2/201110130415Typhoon%20Morakot%20Disaster%20Responses%20Reports_74_20090908_1830.pdf (last access: 10 July 2017).
Nefeslioglu, H.A., Sezer, E., Gokceoglu, C., Bozkir, A.S., Duman, T.Y., 2010. Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Mathematical Problems in Engineering, 2010, 15 pp.
Nichol, J., Wong, M.S., 2005. Satellite remote sensing for detailed landslide inventories using change detection and image fusion. International Journal of Remote Sensing, 26 (9): 1913-1926.
Nourani, V., Pradhan, B., Ghaffari, H., Sharifi, S.S., 2014. Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models. Natural Hazards, 71: 523-547.
Oh, H.-J., Lee, S., 2011. Cross-application used to validate landslide susceptibility maps using a probabilistic model from Korea. Environmental Earth Sciences, 64: 395-409.
Oh, H.-J., Pradhan, B., 2011. Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Computers & Geosciences, 37: 1264-1276.
Ouyang, Y., Luo, S.M., Cui, L.H., Wang, Q., Zhang, J.E., 2011. Estimation of real-time N load in surface water using dynamic data-driven application system. Ecological Engineering, 37(4): 616-621.
Palamakumbure, D., Flentje, P., Stirling, D., 2015. Consideration of optimal pixel resolution in deriving landslide susceptibility zoning within the Sydney Basin, New South Wales, Australia. Computers & Geosciences, 82: 13-22.
Park, D.W., Nikhil, N.V., Lee, S.R., 2013. Landslide and debris flow susceptibility zonation using TRIGRS for the 2011 Seoul landslide event. Natural Hazards and Earth System Sciences, 13: 2833-2849.
Peduzzi, P., 2010. Landslide and vegetation cover in the 2005 Northern Pakistan earthquake: a GIS and statistical quantitative approach. Natural Hazards and Earth System Sciences, 10: 623-640.
Petschko, H., Brenning, A., Bell, R., Goetz, J., Glade, T., 2014. Assessing the quality of landslide susceptibility maps – case study Lower Austria. Natural Hazards and Earth System Sciences, 14: 95-118.
Poli, S., Sterlacchini, S., 2007. Landslide representation strategies in susceptibility studies using weights-of-evidence modelling technique. Natural Resources Research, 16(2): 121-134.
Poudyal, C.P., Chang, C., Oh H.-J., Lee, S., 2010. Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya. Environmental Earth Sciences, 61: 1049-1064.
Pradhan, B., Lee, S., 2010a. Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling. Environmental Modelling and Software, 25: 747-759.
Pradhan, B., Sezer, E.A., Gokceoglu, C., Buchroithner, M.F., 2010b. Landslide susceptibility mapping by neuro-fuzzy approach in a landslide-prone area (Cameron Highlands, Malaysia). IEEE Transactions on Geoscience and Remote Sensing, 48(12): 4164-4177.
Pradhan, B., Lee, S., 2010b. Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides, 7: 13-30.
Pradhan, B., Lee, S., Buchroithner, M.F., 2010a. A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Computers, Environment and Urban Systems, 34: 216-235.
Raia, S., Alvioli, M., Rossi, M., Baum, R. L., Godt, J. W., and Guzzetti, F., 2013. Improving predictive power of physical based rainfall-induced shallow landslide models: a probabilistic approach. Geoscientific Model Development, 6: 1367-1426.
Rodriguez-Galiano, V.F., Ghimire, B., Rogan, J., Chica-Olmo, M., Rigol-Sanchez, J.P., 2012. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67: 93-104.
Rossi, M., Guzzetti, F., Reichenbach, P., Mondini, A.C., Peruccacci, S., 2010. Optimal landslide susceptibility zonation based on multiple forecasts. Geomorphology, 114: 129-142.
Saito, H., Nakayama, D., Matsuyama, H., 2009. Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: the Akaishi Mountains, Japan. Geomorphology, 109: 108-121.
Sakar, S., Kanungo, D.P., 2004. An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogrammetric Engineering & Remote Sensing, 70: 614-625.
Samodra, G., Chen, G., Sartohadi, J., Kasama, K., 2015. Generating landslide inventory by participatory mapping: an example in Purwosari Area, Yogyakarta, Java. Geomorphology, in press.
Schicker, R., Moon, V., 2010. Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale. Geomorphology, 161-162: 40-57.
Schott, J.R., Salvaggio, C., Volchok, W.J., 1988. Radiometric scene normalization using pseudo invariant features. Remote Sensing of Environment, 26:1-16.
Shang, X., Chisholm, L.A., 2014. Classification of Australian native forest species using hyperspectral remote sensing and machine-learning classification algorithms. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7: 2481-2489.
Shao, Y., Campbell, J.B., Taff, G.N., Zheng, B., 2015. An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data. International Journal of Applied Earth Observation and Geoinformation, 38: 78-87.
Simon, N., Crozier, M., de Moiste, M., Rafek, A. G., 2013. Point based assessment: selecting the best way to represent landslide polygon as point frequency in landslide investigation. Electronic Journal of Geotechnical Engineering, 18: 775-784.
Song, K.-Y., Oh, H.-J., Choi, J., Park, I., Lee, C., Lee, S., 2012. Prediction of landslides using ASTER imagery and data mining models. Advances in Space Research, 49: 978-993.
Stumpf, A., Kerle, N., 2011. Object-oriented mapping of landslides using Random Forests. Remote Sensing of Environment, 115: 2564-2577.
Su, C., Wang, L., Wang, X., Huang Z., Zhang, X., 2015. Mapping of rainfall-induced landslide susceptibility in Wencheng, China, using support vector machine. Natural Hazards, 76: 1759-1779.
Suzen, M.L., Doyuran, V., 2004. Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey. Engineering Geology, 71: 303-321.
SWCB, 2011. Development and verification for landslide and debris flow dynamic prediction model (坡地崩塌及土石流動態預測模式開發及驗證). Report (in Chinese).
Tsai, F., Chen, L.C., 2007. Long-term landcover monitoring and disaster assessment in the Shiman reservoir watershed using satellite images. In: Proc. of 13th CeRES International Symposium on Remote Sensing, 29-30 Oct. 2007, Chiba, Japan.
Tsai, F., Hwang, J.-H., Chen, L.-C., Lin, T.-H., 2010. Post-disaster assessment of land-slides in southern Taiwan after 2009 Typhoon Morakot using remote sensing and spatial analysis. Natural Hazards and Earth System Sciences, 10: 2179-2190.
Tsai, F., Lai, J.-S., Chen, W.W., Lin, T.-H., 2013. Analysis of topographic and vegetative factors with data mining for landslide verification. Ecological Engineering, 61: 669-677.
Tsai, F., Lai, J.-S., Lin, T.-H., 2012. Mitigating multi-temporal difference of NDVI by pseudo invariant features normalization for mountain region. In: Proc. of International Symposium on Remote Sensing, 10-12 Oct. 2012, Incheon, Korea.
Tsai, F., Lai, J.-S., Lu, Y.-H., 2016. Full-waveform LiDAR point cloud land cover classification with volumetric texture measures. Terrestrial, Atmospheric and Oceanic Sciences, 27(4): 549-563.
Tsou, C.-Y., Feng, Z.-Y., Chigira, M., 2011. Catastrophic landslide induced by Typhoon Morakot, Shiaolin, Taiwan. Geomorphology, 127: 166-178.
Uprety, P., Yamazaki, F., Dell′ Acqua, F., 2013. Damage detection using high-resolution SAR imagery in the 2009 L′Aquila, Italy, Earthquake. Earthquake Spectra, 29(4): 1521-1535.
Vahidnia, M.H., Alesheikh, A.A., Alimohammadi, A., Hosseinali, F., 2010. A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Computers & Geosciences, 36: 1101-1114.
van den Eeckhaut, M., Reichenbach, P., Guzzetti, F., Rossi, M., Poesen, J., 2009. Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium. Natural Hazards and Earth System Sciences, 9: 507-521.
van Westen, C.J., Castellanos, E., Kuriakose, S. L., 2008. Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Engineering Geology, 102: 112-131.
van Westen, C.J., Lulie, G.F., 2003. Analyzing the evolution of the Tessina landslide using aerial photographs and digital elevation models. Geomorphology, 54(1-2): 77-89.
van Westen, C.J., Soeters, R., Sijmons, K., 2000. Digital geomorphological landslide hazard mapping of the Alpage area, Italy. International Journal of Applied Earth Observation and Geoinformmation, 2(1): 51-59.
van Westen, C.J., van Asch, T.W.J., Soeters, R., 2006. Landslide hazard and risk zonation- why is it still so difficult?. Bulletin of Engineering Geology and the Environment, 65: 167-184.
Varnes, D.J., 1978. Slope movement types and process. In: Schuster RL, Krizek RJ (eds) Landslides, analysis and control, special report 176: Transportation research board, National Academy of Sciences, Washington, DC., pp. 11-33.
Wan, S., 2009. A spatial decision support system for extracting the core factors and thresholds for landslide susceptibility map. Engineering Geology, 108: 237-251.
Wan, S., 2013. Entropy-based particle swarm optimization with clustering analysis on landslide susceptibility mapping. Environmental Earth Sciences, 68: 1349-1366.
Wan, S., Lei, T.C., Chou, T.Y., 2010. A novel data mining technique of analysis and classification for landslide problems. Natural Hazards, 52: 211-230.
Wang, H., Liu, G, Xu, W., Wang, G., 2005. GIS-based landslide hazard assessment: an overview. Progress in Physical Geography, 29(4): 548-567.
Wang, L.-J., Sawada, K., Moriguchi, S., 2013. Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy. Computers & Geosciences, 57: 81-92.
Wang, X., Niu, R., 2009. Spatial forecast of landslides in Three Gorges based on spatial data mining, Sensors, 9: 2035-2061.
Wang, X., Niu, R., 2010. Landslide intelligent prediction using object-oriented method. Soil Dynamics and Earthquake Engineering, 30: 1478-1486.
Wang, X., Zhang, L., Wang, S., Lari, S., 2014. Regional landslide susceptibility zoning with considering the aggregation of landslide points and the weights of factors. Landslides, 11(3): 399-409.
Witten, I.H., Frank, E., Hall, M.A., 2011. Data mining: practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, San Francisco, USA, 605 p.
Wu, C.-H., Chen, S.-C., Chou, H.-T., 2011. Geomorphologic characteristics of catastrophic landslides during typhoon Morakot in the Kaoping Watershed, Taiwan. Engineering Geology, 123: 13-21.
Wu, C.Y., Chen, S.C., 2013. Integrating spatial, temporal, and size probabilities for the annual landslide hazard maps in the Shihmen watershed, Taiwan. Natural Hazards and Earth System Sciences, 13: 2353-2367.
Yilmaz, C., Topal, T., Lűtfi Sűzen, M., 2012.GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey). Environmental Earth Sciences, 65: 2161-2178.
Yilmaz, I., 2009. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat-Turkey). Computers & Geosciences, 35: 1125-1138.
Yilmaz, I., 2010a. Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial networks, and support vector machine. Environmental Earth Sciences, 61: 821-836.
Yilmaz, I., 2010b. The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks, Environmental Earth Sciences, 60: 505-519.
Zhu, A.-X., Wang, R., Qiao, J., Qin, C.-Z., Chen, Y., Liu, J., Du, F., Lin, Y., Zhu, T., 2014. An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic. Geomorphology, 214: 128-138. |