NDLI: Debris flow risk assessment using principal components analysis and rough set techniques

Content Provider	IEEE Xplore Digital Library
Author	Lan Yu Xia Liu
Copyright Year	2014
Description	Author affiliation: Dept. of Inf. Eng., Yunnan Land & Resources Vocational Coll., Kunming, China (Lan Yu; Xia Liu)
Abstract	Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. Thus, developing a method of debris flows assessment seems necessary. Previous researchers have proposed several methods, such as regression analysis, fuzzy mathematics, and artificial neural networks for debris-flow hazard assessment. However, these methods need further improvements to eliminate the high relativity in their results. For improving the assessment of debris flows risk, a hybrid assessment model combined principal components analysis and rough set theory to determine hazard levels of debris flow in regions, with steps like determining hazard-level-type regions. Firstly, we use principal components analysis technique to reduce debris flows data, and attain a data set of attribute reduction. Secondly, we utilize rough set technique to classify the reduction set, and obtain a classification about debris flows risk assessment. Our proposed methodology was then illustrated to assess the regional debris hazard of Yunnan Province in China. The results indicate that the proposed debris flows risk assessment model is accurate and efficient, and can improve the comparability and reliability of the assessment to some degree.
Starting Page	7179
Ending Page	7182
File Size	103664
Page Count	4
File Format	PDF
ISBN	9789881563873
ISSN	19341768
DOI	10.1109/ChiCC.2014.6896186
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	TCCT, CAA
Subject Keyword	Debris flows Rough sets Hazards Terrain factors Eigenvalues and eigenfunctions rough set Risk management Covariance matrices Principal component analysis Decision rule Principle components analysis
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Recent results on sparse principle component analysis

Digital signature to help network management using principal component analysis and K-means clustering

Image authenticity implementing Principal Component Analysis (PCA)

High-dimensional analysis of semidefinite relaxations for sparse principal components

Fault estimation of nonlinear processes using kernel principal component analysis

Driver identification using variance of the acceleration data

Exploiting intelligent decision supports for model-driven biomedical system analysis

Clustering distribution feeders in the Arizona Public Service territory

Estimating the Number of Hidden Neurons of the MLP Using Singular Value Decomposition and Principal Components Analysis: A Novel Approach

Debris flow risk assessment using principal components analysis and rough set techniques

Similar Documents

Recent results on sparse principle component analysis

Digital signature to help network management using principal component analysis and K-means clustering

Image authenticity implementing Principal Component Analysis (PCA)

High-dimensional analysis of semidefinite relaxations for sparse principal components

Fault estimation of nonlinear processes using kernel principal component analysis

Driver identification using variance of the acceleration data

Exploiting intelligent decision supports for model-driven biomedical system analysis

Clustering distribution feeders in the Arizona Public Service territory

Estimating the Number of Hidden Neurons of the MLP Using Singular Value Decomposition and Principal Components Analysis: A Novel Approach

Debris flow risk assessment using principal components analysis and rough set techniques