Geospatial data-driven assessment of earthquake-induced liquefaction impact mapping using classifier and cluster ensembles

[1]  Rômulo Bandeira Pimentel Drumond,et al.  Pattern classification based on regional models , 2022, Appl. Soft Comput..

[2]  Peng Zhao,et al.  Clustering ensemble based on approximate accuracy of the equivalence granularity , 2022, Appl. Soft Comput..

[3]  A. Das,et al.  Graph based ensemble classification for crime report prediction , 2022, Appl. Soft Comput..

[4]  Jingtao Yao,et al.  A shadowed set-based three-way clustering ensemble approach , 2022, International Journal of Machine Learning and Cybernetics.

[5]  O. N. Oyelade,et al.  A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects , 2022, Eng. Appl. Artif. Intell..

[6]  Han-Saem Kim,et al.  Three-dimensional geotechnical-layer mapping in Seoul using borehole database and deep neural network-based model , 2021, Engineering Geology.

[7]  Soufiana Mekouar,et al.  Classifiers selection based on analytic hierarchy process and similarity score for spam identification , 2021, Appl. Soft Comput..

[8]  Hyung-Ik Cho,et al.  Multivariate geotechnical zonation of seismic site effects with clustering-blended model for a city area, South Korea , 2021, Engineering Geology.

[9]  B. Bradley,et al.  Evaluation and modification of geospatial liquefaction models using land damage observational data from the 2010–2011 Canterbury Earthquake Sequence , 2021, Engineering Geology.

[10]  L. Baise,et al.  Local and regional evaluation of liquefaction potential index and liquefaction severity number for liquefaction-induced sand boils in pohang, South Korea , 2020 .

[11]  M. K. Kelesoglu,et al.  Technical guidelines for the assessment of earthquake induced liquefaction hazard at urban scale , 2020, Bulletin of Earthquake Engineering.

[12]  V. Poggi,et al.  An inter-disciplinary and multi-scale approach to assess the spatial variability of ground motion for seismic microzonation: the case study of Cavezzo municipality in Northern Italy , 2020 .

[13]  J. Bommer,et al.  Liquefaction Hazard in the Groningen Region of the Netherlands due to Induced Seismicity , 2020 .

[14]  Seth Guikema,et al.  Artificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs , 2020, Risk analysis : an official publication of the Society for Risk Analysis.

[15]  Xu Li,et al.  Deep learning-based adversarial multi-classifier optimization for cross-domain machinery fault diagnostics , 2020 .

[16]  Jane You,et al.  Hybrid Classifier Ensemble for Imbalanced Data , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[17]  Jonathan P. Stewart,et al.  Next-generation liquefaction database , 2020, Earthquake Spectra.

[18]  J. Bommer,et al.  What is the Smallest Earthquake Magnitude that Needs to be Considered in Assessing Liquefaction Hazard? , 2019, Earthquake Spectra.

[19]  Jidong Zhao,et al.  Multiscale modeling of large deformation in geomechanics , 2019, International Journal for Numerical and Analytical Methods in Geomechanics.

[20]  L. Wotherspoon,et al.  Development of region-specific soil behavior type index correlations for evaluating liquefaction hazard in Christchurch, New Zealand , 2019, Soil Dynamics and Earthquake Engineering.

[21]  Yongquan Zhou,et al.  Automatic data clustering using nature-inspired symbiotic organism search algorithm , 2019, Knowl. Based Syst..

[22]  R. Kayen,et al.  SPT-based probabilistic and deterministic assessment of seismic soil liquefaction triggering hazard , 2018, Soil Dynamics and Earthquake Engineering.

[23]  Philip R. Page,et al.  A Survey on Data Imputation Techniques: Water Distribution System as a Use Case , 2018, IEEE Access.

[24]  Seung Ryeol Lee,et al.  Paleoseismological implications of liquefaction-induced structures caused by the 2017 Pohang Earthquake , 2018, Geosciences Journal.

[25]  Chang-Guk Sun,et al.  Geospatial Assessment of the Post-Earthquake Hazard of the 2017 Pohang Earthquake Considering Seismic Site Effects , 2018, ISPRS Int. J. Geo Inf..

[26]  Laith Mohammad Abualigah,et al.  A new feature selection method to improve the document clustering using particle swarm optimization algorithm , 2017, J. Comput. Sci..

[27]  Joseph Gomes,et al.  MoleculeNet: a benchmark for molecular machine learning† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc02664a , 2017, Chemical science.

[28]  T. Sitharam,et al.  Principles and Practices of Seismic Zonation , 2018 .

[29]  Yuanjie Zheng,et al.  Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model , 2017, Scientific Reports.

[30]  L. Baise,et al.  An Updated Geospatial Liquefaction Model for Global Application , 2017 .

[31]  Mengfen Shen,et al.  On the spatial variability of CPT-based geotechnical parameters for regional liquefaction evaluation , 2017 .

[32]  Michael Thiel,et al.  High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models , 2017, PloS one.

[33]  Jonathan P. Stewart,et al.  PEER-NGL project: Open source global database and model development for the next-generation of liquefaction assessment procedures , 2016 .

[34]  Choong-Ki Chung,et al.  Integrated system for site-specific earthquake hazard assessment with geotechnical spatial grid information based on GIS , 2016, Natural Hazards.

[35]  Oliver-Denzil S. Taylor,et al.  Moving towards an improved index for assessing liquefaction hazard: Lessons from historical data , 2015 .

[36]  Davene J. Daley,et al.  A Geospatial Liquefaction Model for Rapid Response and Loss Estimation , 2015 .

[37]  Y. Sohn,et al.  Miocene tectonic evolution of the basins and fault systems, SE Korea: dextral, simple shear during the East Sea (Sea of Japan) opening , 2015, Journal of the Geological Society.

[38]  Brendon A. Bradley,et al.  Evaluation of the Liquefaction Potential Index for Assessing Liquefaction Hazard in Christchurch, New Zealand , 2014 .

[39]  J. D. Bray,et al.  Assessment of Liquefaction-Induced Land Damage for Residential Christchurch , 2014 .

[40]  Dae-Won Kim,et al.  Feature selection for multi-label classification using multivariate mutual information , 2013, Pattern Recognit. Lett..

[41]  Jiro Kuwano,et al.  A kriging method of interpolation used to map liquefaction potential over alluvial ground , 2013 .

[42]  Motoki Kazama,et al.  Liquefaction in Tohoku district during the 2011 off the Pacific Coast of Tohoku Earthquake , 2012 .

[43]  Ross W. Boulanger,et al.  Examination and Reevalaution of SPT-Based Liquefaction Triggering Case Histories , 2012 .

[44]  Rolando P. Orense,et al.  Relationship between observed liquefaction at Kaiapoi following the 2010 Darfield earthquake and former channels of the Waimakariri River , 2012 .

[45]  M. Saafi,et al.  Comparison of linear and nonlinear kriging methods for characterization and interpolation of soil data , 2012 .

[46]  B. Bradley,et al.  Use of DCP and SASW Tests to Evaluate Liquefaction Potential: Predictions vs. Observations during the Recent New Zealand Earthquakes , 2011 .

[47]  Subhamoy Bhattacharya,et al.  Liquefaction of soil in the Tokyo Bay area from the 2011 Tohoku (Japan) earthquake , 2011 .

[48]  T. Houle,et al.  Statistical Evaluation of a Biomarker , 2010, Anesthesiology.

[49]  Willibald Loiskandl,et al.  Using sequential Gaussian simulation to assess the field-scale spatial uncertainty of soil water content , 2009 .

[50]  Lior Rokach,et al.  A Survey of Feature Selection Techniques , 2009, Encyclopedia of Data Warehousing and Mining.

[51]  Rich Caruana,et al.  Consensus Clusterings , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[52]  D. Wald,et al.  Review Article Topographic Slope as a Proxy for Seismic Site Conditions and Amplification , 2007 .

[53]  Atilla Ansal,et al.  Earthquake-Induced Liquefaction around Marine Structures , 2007 .

[54]  Melanie Hilario,et al.  Knowledge and Information Systems , 2007 .

[55]  Laurie G. Baise,et al.  Liquefaction Hazard Mapping—Statistical and Spatial Characterization of Susceptible Units , 2006 .

[56]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[57]  Ricardo A. Olea,et al.  A six-step practical approach to semivariogram modeling , 2006 .

[58]  Julian J. Bommer,et al.  Modelling liquefaction-induced building damage in earthquake loss estimation , 2006 .

[59]  Douglas Kline,et al.  Revisiting squared-error and cross-entropy functions for training neural network classifiers , 2005, Neural Computing & Applications.

[60]  Candan Gokceoglu,et al.  A liquefaction severity index suggested for engineering practice , 2005 .

[61]  Armen Der Kiureghian,et al.  STANDARD PENETRATION TEST-BASED PROBABILISTIC AND DETERMINISTIC ASSESSMENT OF SEISMIC SOIL LIQUEFACTION POTENTIAL , 2004 .

[62]  Ludmila I. Kuncheva,et al.  Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.

[63]  Chih-Sheng Ku,et al.  A study of the liquefaction risk potential at Yuanlin, Taiwan , 2004 .

[64]  K. Krivoruchko,et al.  Geostatistical Interpolation and Simulation in the Presence of Barriers , 2004 .

[65]  Harun Sonmez,et al.  Modification of the liquefaction potential index and liquefaction susceptibility mapping for a liquefaction-prone area (Inegol,Turkey) , 2003 .

[66]  Ping-Sien Lin,et al.  Simplified cone penetration test-based method for evaluating liquefaction resistance of soils , 2003 .

[67]  P. Robertson,et al.  Estimating liquefaction induced ground settlements from CPT for level ground , 2002 .

[68]  A. Elgamal,et al.  Influence of Permeability on Liquefaction-Induced Shear Deformation , 2002 .

[69]  Kenneth H. Stokoe,et al.  LIQUEFACTION RESISTANCE OF SOILS : SUMMARY REPORT FROM THE 1996 NCEER AND 1998 NCEER / NSF WORKSHOPS ON EVALUATION OF LIQUEFACTION RESISTANCE OF SOILSa , 2001 .

[70]  Jonathan P. Stewart,et al.  Damage Patterns and Foundation Performance in Adapazari , 2000 .

[71]  W. D. Liam Finn,et al.  Liquefaction in Silty Soils: Design and Analysis , 1994 .

[72]  T. Leslie Youd,et al.  Mapping of Liquefaction Severity Index , 1987 .

[73]  M. Raymond Missing Data in Evaluation Research , 1986 .

[74]  T. Gasser,et al.  Residual variance and residual pattern in nonlinear regression , 1986 .

[75]  Toshio Iwasaki,et al.  Soil liquefaction studies in Japan: state-of-the-art , 1986 .

[76]  E. L. Jackson,et al.  Response to Earthquake Hazard , 1981 .

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

[78]  T L Youd,et al.  MAPPING LIQUEFACTIONINDUCED GROUND FAILURE POTENTIAL , 1978 .

[79]  Kenji Ishihara,et al.  Yielding of Overconsolidated Sand and Liquefaction Model under Cyclic Stresses , 1978 .

[80]  I. M. Idriss,et al.  SIMPLIFIED PROCEDURE FOR EVALUATING SOIL LIQUEFACTION POTENTIAL , 1971 .

[81]  L. Penrose The Elementary Statistics of Majority Voting , 1946 .