Classification and Susceptibility Assessment of Debris Flow Based on a Semi-Quantitative Method Combining of the Fuzzy C-Means Algorithm, Factor Analysis and Efficacy Coefficient

doi 10.5194/nhess-2020-5
Full Text
Abstract

Available in full text

Date
Authors
Publisher

Copernicus GmbH

Related search

Identification of Laser Weld Penetration Based on Fuzzy C-Means Algorithm

2012English

A Multivariate Statistical Method for Susceptibility Analysis of the Debris Flow in Southwest China

2019English

A New Semi-Supervised Intuitionistic Fuzzy C-Means Clustering

ICST Transactions on Scalable Information Systems
2018English

Analysis of Segmentation Parameters Effect Towards Parallel Processing Time on Fuzzy C Means Algorithm

IJITEE (International Journal of Information Technology and Electrical Engineering)
2018English

Enhancement of Fuzzy Possibilistic C-Means Algorithm Using EM Algorithm (EMFPCM)

International Journal of Computer Applications
2013English

A Novel Threshold Technique and Fuzzy C-Means Algorithm for Segmentation of Wound

International Journal of Computer Applications
2013English

Ameliorate Fuzzy C-Means: An Ameliorate Fuzzy C-Means Clustering Algorithm for Ct-Lung Image Segmentation

American Journal of Applied Sciences
Multidisciplinary
2013English

Locating Tumours in the MRI Image of the Brain by Using Pattern Based K-Means and Fuzzy C-Means Clustering Algorithm

International Journal for Research in Applied Science and Engineering Technology
2017English

Comparison of AIS and Fuzzy C-Means Clustering Methods on the Classification of Breast Cancer and Diabetes Datasets

Turkish Journal of Electrical Engineering and Computer Sciences
Electronic EngineeringElectricalComputer Science
2014English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy