Ensemble Disagreement Active Learning for Spatial Prediction of Shallow Landslide

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.

[1]  Dieu Tien Bui,et al.  Application of support vector machines in landslide susceptibility assessment for the Hoa Binh province (Vietnam) with kernel functions analysis , 2012 .

[2]  B. Pradhan,et al.  Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran , 2013, Journal of Earth System Science.

[3]  T. Kavzoglu,et al.  Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression , 2016 .

[4]  A. Akgun A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey , 2012, Landslides.

[5]  Andrea G. Fabbri,et al.  Validation of Spatial Prediction Models for Landslide Hazard Mapping , 2003 .

[6]  I. Moore,et al.  Digital terrain modelling: A review of hydrological, geomorphological, and biological applications , 1991 .

[7]  F. Guzzetti,et al.  Landslide inventory maps: New tools for an old problem , 2012 .

[8]  E. Yesilnacar,et al.  Landslide susceptibility mapping : A comparison of logistic regression and neural networks methods in a medium scale study, Hendek Region (Turkey) , 2005 .

[9]  Lorenzo Bruzzone,et al.  A Novel Active Learning Method in Relevance Feedback for Content-Based Remote Sensing Image Retrieval , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[10]  L. Ayalew,et al.  The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan , 2005 .

[11]  Chong Xu,et al.  GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China , 2012 .

[12]  Bisheng Yang,et al.  Extracting buildings from airborne laser scanning point clouds using a marked point process , 2014 .

[13]  L. Tham,et al.  Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China , 2008 .

[14]  B. Pradhan,et al.  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 , 2013, Arabian Journal of Geosciences.

[15]  Lawrence Carin,et al.  Logistic regression with an auxiliary data source , 2005, ICML.

[16]  B. Pradhan,et al.  Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia , 2010 .

[17]  G. Monette,et al.  Generalized Collinearity Diagnostics , 1992 .

[18]  K. Beven,et al.  A physically based, variable contributing area model of basin hydrology , 1979 .

[19]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[20]  N. Intarawichian,et al.  Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand , 2011 .

[21]  M. Marjanović,et al.  Landslide susceptibility assessment using SVM machine learning algorithm , 2011 .

[22]  B. Pradhan Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches , 2010 .

[23]  Hyun-Joo Oh,et al.  Quantitative landslide susceptibility mapping at Pemalang area, Indonesia , 2010 .

[24]  Guo-feng Zhu,et al.  Relationship between sub-cloud secondary evaporation and stable isotope in precipitation in different regions of China , 2016, Environmental Earth Sciences.

[25]  B. Pradhan,et al.  Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models , 2012 .

[26]  William J. Emery,et al.  Active Learning Methods for Remote Sensing Image Classification , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Melba M. Crawford,et al.  View Generation for Multiview Maximum Disagreement Based Active Learning for Hyperspectral Image Classification , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[28]  A. Ozdemir,et al.  Sinkhole susceptibility mapping using logistic regression in Karapınar (Konya, Turkey) , 2016, Bulletin of Engineering Geology and the Environment.

[29]  Z. A. Latif,et al.  Landslide susceptibility mapping using LiDAR derived factors and frequency ratio model: Ulu Klang area, Malaysia , 2012, 2012 IEEE 8th International Colloquium on Signal Processing and its Applications.

[30]  Stefan Wrobel,et al.  Multi-class Ensemble-Based Active Learning , 2006, ECML.

[31]  Andrew Thomas Hudak,et al.  A Multiscale Curvature Algorithm for Classifying Discrete Return LiDAR in Forested Environments , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[32]  Cristiano Ballabio,et al.  Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy , 2012, Mathematical Geosciences.

[33]  Manfred F. Buchroithner,et al.  Landslide Susceptibility Mapping by Neuro-Fuzzy Approach in a Landslide-Prone Area (Cameron Highlands, Malaysia) , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[34]  Zili Zhang,et al.  Missing Value Estimation for Mixed-Attribute Data Sets , 2011, IEEE Transactions on Knowledge and Data Engineering.

[35]  A. Rezaei,et al.  Landslide susceptibility mapping by combining the three methods Fuzzy Logic, Frequency Ratio and Analytical Hierarchy Process in Dozain basin , 2014 .

[36]  B. Pradhan,et al.  Landslide susceptibility mapping at Golestan Province, Iran: A comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models , 2012 .

[37]  G. Rawat,et al.  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 , 2009 .

[38]  S. Bai,et al.  GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China , 2010 .

[39]  Xueling Wu,et al.  Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China , 2014 .

[40]  William J. Emery,et al.  SVM Active Learning Approach for Image Classification Using Spatial Information , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[41]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[42]  Xiwei Xu,et al.  Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China , 2012, Comput. Geosci..

[43]  A. Stein,et al.  Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India) , 2010 .

[44]  A. Pradhan,et al.  Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-ri Creek, South Korea , 2014, Natural Hazards.

[45]  Mustafa Neamah Jebur,et al.  Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale , 2014 .

[46]  H. Sebastian Seung,et al.  Query by committee , 1992, COLT '92.

[47]  A. Ozdemir,et al.  A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey , 2013 .

[48]  Mustafa Neamah Jebur,et al.  Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines , 2015, Environmental Earth Sciences.

[49]  Saro Lee,et al.  Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models , 2006 .