Method for Mid-Long-Term Prediction of Landslides Movements Based on Optimized Apriori Algorithm

[1]  Venkata Sangameswar Mandavilli,et al.  Detection of natural disaster affected areas using R , 2018 .

[2]  Antonio D’Ambrosio,et al.  Analysis of powered two-wheeler crashes in Italy by classification trees and rules discovery. , 2012, Accident; analysis and prevention.

[3]  Xianmin Wang,et al.  Landslide intelligent prediction using object-oriented method , 2010 .

[4]  Mykola Pechenizkiy,et al.  Speeding-Up Association Rule Mining With Inverted Index Compression , 2016, IEEE Transactions on Cybernetics.

[5]  Shiho Asano,et al.  Relationship between rain and/or meltwater, pore-water pressure and displacement of a reactivated landslide , 2008 .

[6]  Hongzhi Wang,et al.  Trajectory Big Data Processing Based on Frequent Activity , 2019 .

[7]  Jie Wu,et al.  Dache: A data aware caching for big-data applications using the MapReduce framework , 2014 .

[8]  Mykola Pechenizkiy,et al.  Apriori Versions Based on MapReduce for Mining Frequent Patterns on Big Data , 2018, IEEE Transactions on Cybernetics.

[9]  Kaixiang Zhang,et al.  Application of a two-step cluster analysis and the Apriori algorithm to classify the deformation states of two typical colluvial landslides in the Three Gorges, China , 2016, Environmental Earth Sciences.

[10]  P. Versace,et al.  A comprehensive framework for empirical modeling of landslides induced by rainfall: the Generalized FLaIR Model (GFM) , 2017, Landslides.

[11]  Junwei Ma,et al.  Identification of causal factors for the Majiagou landslide using modern data mining methods , 2017, Landslides.

[12]  B. McGlynn,et al.  Hierarchical controls on runoff generation: Topographically driven hydrologic connectivity, geology, and vegetation , 2011 .

[13]  H. Saito,et al.  Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: The Akaishi Mountains, Japan , 2009 .

[14]  F. Wei,et al.  RETRACTED ARTICLE: Distribution characteristics of debris flows and landslides in Three Parallel Rivers Area , 2012, Natural Hazards.

[15]  W. Ning,et al.  Changes in Livestock Migration Patterns in a Tibetan-style Agropastoral System , 2007 .

[16]  Chia-Wen Liao,et al.  Data mining for occupational injuries in the Taiwan construction industry , 2008 .

[17]  W. Z. Savage,et al.  Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning , 2008 .

[18]  Bo Chai,et al.  The Application of Long Short-Term Memory(LSTM) Method on Displacement Prediction of Multifactor-Induced Landslides , 2019, IEEE Access.

[19]  L. Ermini,et al.  Artificial Neural Networks applied to landslide susceptibility assessment , 2005 .

[20]  Kyoji Sassa,et al.  Factors affecting rainfall-induced flowslides in laboratory flume tests , 2001 .

[21]  Hossein Shafizadeh-Moghadam,et al.  Big data in Geohazard; pattern mining and large scale analysis of landslides in Iran , 2018, Earth Science Informatics.

[22]  Biswajeet Pradhan,et al.  A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping , 2014, Landslides.

[23]  Ying Cao,et al.  Application of time series analysis and PSO–SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China , 2016 .