Susceptibility zoning of karst geological hazards using machine learning and cloud model

Susceptibility zoning of karst geological hazards using machine learning and cloud model

Accurate assessment on serious geological hazards using artificial intelligence is required in geological hazards assessment with aim to improve the efficiency and accuracy of karst geological hazard identification. In this paper, topography and geology characteristics are selected as susceptibility...

Full description

Saved in:
Bibliographic Details
Published in:Cluster computing Vol. 22; no. Suppl 4; pp. 8051 - 8058
Main Authors: Xingli, Jia, Qingmiao, Dai, Hongzhi, Yang
Format: Journal Article
Language:English
Published: New York Springer US 01-07-2019
Springer Nature B.V
Subjects:
Artificial intelligence
Classification
Computer Communication Networks
Computer Science
Disasters
Geographic information systems
Geological hazards
Geology
Hazard assessment
Hazard identification
Infrastructure
Karst
Landforms
Learning theory
Lithology
Machine learning
Operating Systems
Processor Architectures
Risk assessment
Roads & highways
Rocks
Seismic engineering
Support vector machines
Zoning
China
Karst geological hazards
SVM
Cloud model
Susceptibility zoning
Artificial intelligence
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Accurate assessment on serious geological hazards using artificial intelligence is required in geological hazards assessment with aim to improve the efficiency and accuracy of karst geological hazard identification. In this paper, topography and geology characteristics are selected as susceptibility factors for geological disasters identification. The area of Guibei, Guangxi Province, was selected as the study area because of its typical karst landform. Intelligent classification of influential factors for karst geological hazard identification is realized by utilization of support vector machine, meanwhile, the cloud model theory was introduced and a comprehensive method of susceptibility zoning deployment of geological hazards in karst areas was proposed, further, a quantitative susceptibility assessment model for unsuitable geology in karst areas was established. After that, a comprehensive geological hazard susceptibility index (S) proposed in this paper is calculated with assistance of machine learning theory in the proposed model. The results show that the influential factors can be divided into five grades through statistical learning, the susceptibility index of geological hazards in the study area is between 0.713 and 5.798, the susceptibility of geological hazards in the mid-section take the largest share. Differences in the distribution of high, middle, and low susceptibility zones are significant. Different susceptibility zoning have different influence on engineering construction.
ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-017-1590-0