Spatial Prediction of Rainfall-Induced Landslides Using Aggregating One-Dependence Estimators Classifier

被引:81
作者
Binh Thai Pham [1 ]
Prakash, Indra [2 ]
Jaafari, Abolfazl [3 ]
Dieu Tien Bui [4 ,5 ]
机构
[1] Univ Transport Technol, Geotech Engn & Artificial Intelligence Res Grp GE, 54 Trieu Khuc, Hanoi, Vietnam
[2] Govt Gujarat, Dept Sci & Technol, BISAG, Gandhinagar, India
[3] Islamic Azad Univ, Karaj Branch, Young Researchers & Elite Club, Karaj, Iran
[4] Ton Duc Thang Univ, Geog Informat Sci Res Grp, Ho Chi Minh City, Vietnam
[5] Ton Duc Thang Univ, Fac Environm & Labour Safety, Ho Chi Minh City, Vietnam
关键词
Landslides; Aggregating One Dependence Estimators; GIS; India; SUPPORT VECTOR MACHINE; ARTIFICIAL NEURAL-NETWORKS; LOGISTIC-REGRESSION; FREQUENCY RATIO; NAIVE BAYES; SUSCEPTIBILITY ASSESSMENT; GIS; MODELS; HAZARD; DECISION;
D O I
10.1007/s12524-018-0791-1
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In this study, the spatial prediction of rainfall-induced landslides at the Pauri Gahwal area, Uttarakhand, India has been done using Aggregating One-Dependence Estimators (AODE) classifier which has not been applied earlier for landslide problems. Historical landslide locations have been collated with a set of influencing factors for landslide spatial analysis. The performance of the AODE model has been assessed using statistical analyzing methods and receiver operating characteristic curve technique. The predictive capability of the AODE model has also been compared with other popular landslide models namely Support Vector Machines (SVM), Radial Basis Function Neural Network (ANN-RBF), Logistic Regression (LR), and Naive Bayes (NB). The result of analysis illustrates that the AODE model has highest predictability, followed by the SVM model, the ANN-RBF model, the LR model, and the NB model, respectively. Thus AODE is a promising method for the development of better landslide susceptibility map for proper landslide hazard management.
引用
收藏
页码:1457 / 1470
页数:14
相关论文
共 78 条
[1]   A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at Izmir, Turkey [J].
Akgun, Aykut .
LANDSLIDES, 2012, 9 (01) :93-106
[2]   Modeling and Testing Landslide Hazard Using Decision Tree [J].
Alkhasawneh, Mutasem Sh. ;
Ngah, Umi Kalthum ;
Tay, Lea Tien ;
Isa, Nor Ashidi Mat ;
Al-Batah, Mohammad Subhi .
JOURNAL OF APPLIED MATHEMATICS, 2014,
[3]  
[Anonymous], 2006, LANDSLIDE HAZARD RIS
[4]  
[Anonymous], 1975, SIGNAL DETECTION THE
[5]  
[Anonymous], 2000, TERRAIN ANAL PRINCIP
[6]  
[Anonymous], 2016, J. Geomatics
[7]  
[Anonymous], 2008, Landslides-Disaster Risk Reduction
[8]   Autologistic modelling of susceptibility to landsliding in the Central Apennines, Italy [J].
Atkinson, P. M. ;
Massari, R. .
GEOMORPHOLOGY, 2011, 130 (1-2) :55-64
[9]   The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan [J].
Ayalew, L ;
Yamagishi, H .
GEOMORPHOLOGY, 2005, 65 (1-2) :15-31
[10]   GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang, China [J].
Bai, Shibiao ;
Lue, Guonian ;
Wang, Jian ;
Zhou, Pinggen ;
Ding, Liang .
ENVIRONMENTAL EARTH SCIENCES, 2011, 62 (01) :139-149