Prediction model of collapse risk based on information entropy and distance discriminant analysis method
type: Item
instance of: scholarly article
title: Prediction model of collapse risk based on information entropy and distance discriminant analysis method
zbMATH Open document ID: 1426.62384
DOI: 10.1155/2017/8793632
author: Hujun He
author: Le An
author: Jian Zhang
author: Wei Liu
published in: Mathematical Problems in Engineering
publication date: 2018-11-05T00:00:00Z
review text: Summary: The prediction and risk classification of collapse is an important issue in the process of highway construction in mountainous regions. Based on the principles of information entropy and Mahalanobis distance discriminant analysis, we have produced a collapse hazard prediction model. We used the entropy measure method to reduce the influence indexes of the collapse activity and extracted the nine main indexes affecting collapse activity as the discriminant factors of the distance discriminant analysis model (i.e., slope shape, aspect, gradient, and height, along with exposure of the structural face, stratum lithology, relationship between weakness face and free face, vegetation cover rate, and degree of rock weathering). We employ postearthquake collapse data in relation to construction of the Yingxiu-Wolong highway, Hanchuan County, China, as training samples for analysis. The results were analyzed using the back substitution estimation method, showing high accuracy and no errors, and were the same as the prediction result of uncertainty measure. Results show that the classification model based on information entropy and distance discriminant analysis achieves the purpose of index optimization and has excellent performance, high prediction accuracy, and a zero false-positive rate. The model can be used as a tool for future evaluation of collapse risk.
Mathematics Subject Classification ID: 62P30
Mathematics Subject Classification ID: 62H30
zbMATH DE Number: 6972621
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/8793632
OpenAlex ID: W2748200437
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description: scientific article; zbMATH DE number 6972621