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Ensemble Disagreement Active Learning for Spatial Prediction of Shallow Landslide

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Laser Scanning Applications in Landslide Assessment

Abstract

In Malaysia, landslides are considered as the most frequent and devastating natural disaster that cause human life and property losses. The spatial prediction of landslides is the basic step required for hazard and risk assessments. Spatial prediction methods of landslides are established and documented in the literature. However, several research directions on this topic need to be developed and explored in depth. The current improvement in computer technology and laser scanning systems provide improved data processing capabilities and topographic datasets, as well as new trends in landslide modeling and methods that can deal with such advanced technologies and datasets.

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References

  • Akgun, A. (2012). A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: A case study at İzmir, Turkey. Landslides, 9(1), 93–106.

    Article  Google Scholar 

  • Ayalew, L., & Yamagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65(1), 15–31.

    Article  Google Scholar 

  • Bai, S. B., Wang, J., Lü, G. N., Zhou, P. G., Hou, S. S., & Xu, S. N. (2010). GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the three Gorges area, China. Geomorphology, 115(1), 23–31.

    Article  Google Scholar 

  • Ballabio, C., & Sterlacchini, S. (2012). Support vector machines for landslide susceptibility mapping: the Staffora River Basin case study, Italy. Mathematical Geosciences, 44(1), 47–70.

    Article  Google Scholar 

  • Beven, K. J., & Kirkby, M. J. (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. Hydrological Sciences Journal, 24(1), 43–69.

    Article  Google Scholar 

  • Bui, D. T., Pradhan, B., Lofman, O., Revhaug, I., & Dick, O. B. (2012). Application of support vector machines in landslide susceptibility assessment for the Hoa Binh province (Vietnam) with kernel functions analysis (Doctoral dissertation, International Environmental Modelling and Software Society (iEMSs)).

    Google Scholar 

  • Chung, C. J. F., & Fabbri, A. G. (2003). Validation of spatial prediction models for landslide hazard mapping. Natural Hazards, 30(3), 451–472.

    Article  Google Scholar 

  • Colkesen, I., Sahin, E. K., & Kavzoglu, T. (2016). Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression. Journal of African Earth Sciences, 118, 53–64.

    Article  Google Scholar 

  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273–297.

    Google Scholar 

  • Das, I., Sahoo, S., van Westen, C., Stein, A., & Hack, R. (2010). Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India). Geomorphology, 114(4), 627–637.

    Article  Google Scholar 

  • Demir, B., & Bruzzone, L. (2015). A novel active learning method in relevance feedback for content-based remote sensing image retrieval. IEEE Transactions on Geoscience and Remote Sensing, 53(5), 2323–2334.

    Article  Google Scholar 

  • Di, W., & Crawford, M. M. (2012). View generation for multiview maximum disagreement based active learning for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 50(5), 1942–1954.

    Article  Google Scholar 

  • Evans, J. S., & Hudak, A. T. (2007). A multiscale curvature algorithm for classifying discrete return LiDAR in forested environments. IEEE Transactions on Geoscience and Remote Sensing, 45(4), 1029–1038.

    Article  Google Scholar 

  • Fox, J., & Monette, G. (1992). Generalized collinearity diagnostics. Journal of the American Statistical Association, 87(417), 178–183.

    Article  Google Scholar 

  • 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(1), 42–66.

    Article  Google Scholar 

  • Hong, H., Pradhan, B., Jebur, M. N., Bui, D. T., Xu, C., & Akgun, A. (2016). Spatial prediction of landslide hazard at the luxi area (china) using support vector machines. Environmental Earth Sciences, 75(1), 1–14.

    Article  CAS  Google Scholar 

  • Hyun-Joo, O., Saro, L., & Soedradjat, G. M. (2010). Quantitative landslide susceptibility mapping at Pemalang area. Indonesia. Environmental Earth Sciences, 60(6), 1317–1328.

    Article  Google Scholar 

  • Intarawichian, N., & Dasananda, S. (2011). Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand. Environmental Earth Sciences, 64(8), 2271–2285.

    Article  Google Scholar 

  • Jebur, M. N., Pradhan, B., & Tehrany, M. S. (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.

    Article  Google Scholar 

  • Körner, C., & Wrobel, S. (2006). Multi-class ensemble-based active learning. In European conference on machine learning (pp. 687–694). Berlin, Heidelberg: Springer.

    Google Scholar 

  • Lateh, H., Jefriz, Muhiyuddin, W. M., Taib, B., & Khan, Y. A. (2010). Monitoring of hill-slope movement due to rainfall at Gunung Pass of Cameron Highland district of Peninsular Malaysia. International Journal of Earth Sciences and Engineering, 3, 6–12.

    Google Scholar 

  • Latif, Z. A., Aman, S. N. A., & Pradhan, B. (2012, March). Landslide susceptibility mapping using LiDAR derived factors and frequency ratio model: Ulu Klang area, Malaysia. In 2012 IEEE 8th International Colloquium on Signal Processing and its Applications (CSPA) (pp. 378–382).

    Google Scholar 

  • Lee, S., & Sambath, T. (2006). Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environmental Geology, 50(6), 847–855.

    Article  Google Scholar 

  • Liao, X., Xue, Y., & Carin, L. (2005, August). Logistic regression with an auxiliary data source. In Proceedings of the 22nd international conference on Machine learning (pp. 505-512). USA: ACM.

    Google Scholar 

  • Marjanović, M., Kovačević, M., Bajat, B., & Voženílek, V. (2011). Landslide susceptibility assessment using SVM machine learning algorithm. Engineering Geology, 123(3), 225–234.

    Article  Google Scholar 

  • Mathew, J., Jha, V. K., & Rawat, G. S. (2009). Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method. Landslides, 6(1), 17–26.

    Article  Google Scholar 

  • Mohammady, M., Pourghasemi, H. R., & Pradhan, B. (2012). Landslide susceptibility mapping at Golestan Province, Iran: A comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models. Journal of Asian Earth Sciences, 61, 221–236.

    Article  Google Scholar 

  • Moore, I. D., Grayson, R. B., & Ladson, A. R. (1991). Digital terrain modelling: A review of hydrological, geomorphological, and biological applications. Hydrological Processes, 5(1), 3–30.

    Article  Google Scholar 

  • Ozdemir, A. (2016). Sinkhole susceptibility mapping using logistic regression in Karapınar (Konya, Turkey). Bulletin of Engineering Geology and the Environment, 75(2), 681–707.

    Article  CAS  Google Scholar 

  • Ozdemir, A., & Altural, T. (2013). A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. Journal of Asian Earth Sciences, 64, 180–197.

    Article  Google Scholar 

  • Pasolli, E., Melgani, F., Tuia, D., Pacifici, F., & Emery, W. J. (2014). SVM active learning approach for image classification using spatial information. IEEE Transactions on Geoscience and Remote Sensing, 52(4), 2217–2233.

    Article  Google Scholar 

  • Peng, L., Niu, R., Huang, B., Wu, X., Zhao, Y., & Ye, R. (2014). Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China. Geomorphology, 204, 287–301.

    Article  Google Scholar 

  • Pourghasemi, H. R., Jirandeh, A. G., Pradhan, B., Xu, C., & Gokceoglu, C. (2013). Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. Journal of Earth System Science, 122(2), 349–369.

    Article  Google Scholar 

  • Pradhan, A. M. S., & Kim, Y. T. (2014). Relative effect method of landslide susceptibility zonation in weathered granite soil: A case study in Deokjeokri Creek, South Korea. Natural hazards, 72(2), 1189–1217.

    Article  Google Scholar 

  • Pradhan, B. (2010). Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. Journal of the Indian Society of Remote Sensing, 38(2), 301–320.

    Article  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.

    Article  Google Scholar 

  • Pradhan, B., Sezer, E. A., Gokceoglu, C., & Buchroithner, M. F. (2010). 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.

    Article  Google Scholar 

  • Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In Proceedings of the fifth annual workshop on Computational learning theory (pp. 287–294). USA: ACM.

    Google Scholar 

  • Tazik, E., Jahantab, Z., Bakhtiari, M., Rezaei, A., & Alavipanah, S. K. (2014). Landslide susceptibility mapping by combining the three methods fuzzy logic, frequency ratio and analytical hierarchy process in Dozain basin. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(2), 267.

    Article  Google Scholar 

  • Tien Bui, D., Pradhan, B., Lofman, O., & Revhaug, I. (2012). Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and Naive Bayes models. Mathematical Problems in Engineering, 2012.

    Google Scholar 

  • Tuia, D., Ratle, F., Pacifici, F., Kanevski, M. F., & Emery, W. J. (2009). Active learning methods for remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 47(7), 2218–2232.

    Article  Google Scholar 

  • Vapnik, V. (1995). The nature of statistical learning. New York: Springer.

    Book  Google Scholar 

  • Xu, C., Dai, F., Xu, X., & Lee, Y. H. (2012a). GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology, 145, 70–80.

    Article  Google Scholar 

  • Xu, C., Xu, X., Dai, F., & Saraf, A. K. (2012b). Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China. Computers & Geosciences, 46, 317–329.

    Article  CAS  Google Scholar 

  • Yang, B., Xu, W., & Yao, W. (2014). Extracting buildings from airborne laser scanning point clouds using a marked point process. GIScience & Remote Sensing, 51(5), 555–574.

    Article  Google Scholar 

  • Yao, X., Tham, L. G., & Dai, F. C. (2008). Landslide susceptibility mapping based on support vector machine: A case study on natural slopes of Hong Kong. China. Geomorphology, 101(4), 572–582.

    Article  Google Scholar 

  • Yesilnacar, E., & Topal, T. (2005). Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Engineering Geology, 79(3), 251–266.

    Article  Google Scholar 

  • Zare, M., Pourghasemi, H. R., Vafakhah, M., & Pradhan, B. (2013). Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: A comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arabian Journal of Geosciences, 6(8), 2873–2888.

    Article  Google Scholar 

  • Zhu, X., Zhang, S., Jin, Z., Zhang, Z., & Xu, Z. (2011). Missing value estimation for mixed-attribute data sets. IEEE Transactions on Knowledge and Data Engineering, 23(1), 110–121.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, University Putra Malaysia, Serdang, Malaysia

    Biswajeet Pradhan, Maher Ibrahim Sameen & Bahareh Kalantar

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  1. Biswajeet Pradhan
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  2. Maher Ibrahim Sameen
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  3. Bahareh Kalantar
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Correspondence to Biswajeet Pradhan .

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  1. Department of Civil Engineering, University Putra Malaysia, Seri Kembangan, Malaysia

    Biswajeet Pradhan

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Pradhan, B., Sameen, M.I., Kalantar, B. (2017). Ensemble Disagreement Active Learning for Spatial Prediction of Shallow Landslide. In: Pradhan, B. (eds) Laser Scanning Applications in Landslide Assessment. Springer, Cham. https://doi.org/10.1007/978-3-319-55342-9_10

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