scholarly journals Prediction Model of Collapse Risk Based on Information Entropy and Distance Discriminant Analysis Method

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Hujun He ◽  
Le An ◽  
Wei Liu ◽  
Jian Zhang
Keyword(s):  
Discriminant Analysis ◽  
Prediction Model ◽  
Information Entropy ◽  
False Positive Rate ◽  
Estimation Method ◽  
Classification Model ◽  
Entropy Measure ◽  
Analysis Model ◽  
Mountainous Regions ◽  
Distance Discriminant Analysis

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.

Instability Identification of Goaf Risky in Mines Based on Distance Discriminant Analysis Method

2011 ◽  
Vol 255-260 ◽  
pp. 3740-3743
Author(s):  
Xiao Ming Yan ◽  
Xiang Yun Chen ◽  
Feng Qiang Gong
Keyword(s):  
Discriminant Analysis ◽  
Body Volume ◽  
Analysis Method ◽  
Analysis Model ◽  
Metal Mine ◽  
Ore Body ◽  
Rock Quality ◽  
Training Samples ◽  
Distance Discriminant Analysis ◽  
The Stability

The instability identification of goaf risky is an important work in the mine engineering. Based on distance discriminant analysis, a method to identify the instability of goaf risky in mines was presented in this paper. Nine factors influencing the stability of goaf risky, including uniaxial compressive strength of rock, elastic modulus of rock, rock quality designation (RQD), area ration of pillar, ratio of width to height of pillar, depth of ore body, volume of goaf, dip of ore body and area of goaf, were selected as discriminant indexes in the stability analysis of goaf. The actual data of 40 goafs were used as training samples to establish a discriminant analysis model to identify the stability of goaf. The results show that this discriminant analysis model has high precision and misdiscriminant ratio is 0.025 in re-substitution process. The instability identification of a metal mine was discriminanted by using this model and the identification result is identical with that of practical situation.

scholarly journals A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 700
Author(s):  
Belén Pérez-Sánchez ◽  
Martín González ◽  
Carmen Perea ◽  
Jose J. López-Espín
Keyword(s):  
Experimental Study ◽  
Computational Study ◽  
Estimation Method ◽  
Information Criteria ◽  
Simultaneous Equations ◽  
Estimation Methods ◽  
Entropy Measure ◽  
Economic Science ◽  
Akaike Information Criteria ◽  
Simultaneous Equations Models

Simultaneous Equations Models (SEM) is a statistical technique widely used in economic science to model the simultaneity relationship between variables. In the past years, this technique has also been used in other fields such as psychology or medicine. Thus, the development of new estimating methods is an important line of research. In fact, if we want to apply the SEM to medical problems with the main goal being to obtain the best approximation between the parameters of model and their estimations. This paper shows a computational study between different methods for estimating simultaneous equations models as well as a new method which allows the estimation of those parameters based on the optimization of the Bayesian Method of Moments and minimizing the Akaike Information Criteria. In addition, an entropy measure has been calculated as a parameter criteria to compare the estimation methods studied. The comparison between those methods is performed through an experimental study using randomly generated models. The experimental study compares the estimations obtained by the different methods as well as the efficiency when comparing solutions by Akaike Information Criteria and Entropy Measure. The study shows that the proposed estimation method offered better approximations and the entropy measured results more efficiently than the rest.

scholarly journals MODELO DE PREVISÃO DE DESEMPENHO DE TRIATLETAS COM A UTILIZAÇÃO DA ANÁLISE DISCRIMINANTE (TRIATHLETES PERFORMANCE PREDICTION MODEL USING DISCRIMINANT ANALYSIS)

2017 ◽  
Vol 10 ◽  
pp. 111-118
Author(s):  
Domingos Rodrigues Pandeló Júnior
Keyword(s):  
Discriminant Analysis ◽  
Prediction Model ◽  
Performance Prediction ◽  
A Performance

A previsão do desempenho esportivo é relevante para a identificação de talentos e no estabelecimento de estratégias de treinamento. O objetivo do presente artigo é estabelecer um modelo que tenha a capacidade de predizer o desempenho de triatletas. Para isso foi utilizada a análise discriminante, que é uma técnica de análise multivariada. Trabalhou-se com 21 voluntários, sendo 7 profissionais e 14 amadores, todos do sexo masculino. Foram selecionadas variáveis antropométricas, fisiológicas e de treinamento fáceis de serem mensuradas, sem a necessidade de utilização de laboratórios específicos. O presente estudo mostrou que com a utilização de algumas variáveis pode-se buscar inferir a performance de triatletas. A previsão de performance é de vital importância, quer seja para a detecção de talentos, quer seja para a estruturação do treinamento, o que mostra a importância do desenvolvimento de modelos desse tipo.

scholarly journals Research on Liquor Storage Based on Statistical Analysis Model

2021 ◽  
Vol 5 (3) ◽  
Author(s):  
Xuhua Xu
Keyword(s):  
Index System ◽  
Comprehensive Evaluation ◽  
Principal Component ◽  
Evaluation Index System ◽  
Classification Model ◽  
Analysis Model ◽  
The Index System ◽  
Storage Methods ◽  
Physical And Chemical

According to the physical and chemical indexes of different periods, different storage containers and different storage sites, a two overall T test was used to show that there were significant differences in physical and chemical indexes of liquor body in different detection sites. Correlation analysis of wine storage in different storage methods by Spearman correlation coefficient. By using the principal component analysis method, the comprehensive evaluation index system of the quality of the wine body was constructed, and the classification model of the detection location based on the comprehensive evaluation was established by using the index system. The detection sites were classified, and the results showed that the detection sites were divided into four grades.

scholarly journals Research on the Construction of Big Data Classification System Based on Distributed Data Flow

2021 ◽  
Vol 2136 (1) ◽  
pp. 012057
Author(s):  
Han Zhou
Keyword(s):  
Big Data ◽  
New Technologies ◽  
Data Classification ◽  
Classification Model ◽  
Distributed Data ◽  
Analysis Model ◽  
Database Construction ◽  
Big Data Classification ◽  
Operation Status ◽  
Model Algorithm

Abstract In the context of the comprehensive popularization of network technical services and database construction system, more and more data are used by enterprises or individuals. It is difficult for the existing technology to meet the technical analysis requirements of the development of the era of big data. Therefore, in the development of practice, we should continue to explore new technologies and methods to reasonably use big data. Therefore, on the basis of understanding the current big data technology and its system operation status, this paper designs relevant algorithms according to the big data classification model, and verifies the effectiveness of the analysis model algorithm based on practice.

Development of the Hypertension Index Model in General Adult Using the Korea National Health and Nutritional Examination Survey and the Korean Genome and Epidemiology Study (Preprint)

2021 ◽  
Author(s):  
Myung Jae Seo ◽  
Sung Gyun Ahn ◽  
Yong-Jae Lee ◽  
Jong Koo Kim
Keyword(s):  
Prediction Model ◽  
National Health ◽  
Prediction Models ◽  
Classification Model ◽  
Nutrition Examination Survey ◽  
Epidemiology Study ◽  
Index Model ◽  
Health And Nutrition ◽  
Specific Prediction ◽  
And Control

BACKGROUND Hypertension, a risk factor for cardiovascular disease and all-cause mortality, has been increasing. Along with emphasizing awareness and control of hypertension, predicting the incidence of hypertension is important. Several studies have previously reported prediction models of hypertension. However, among the previous models for predicting hypertension, few models reflect various risk factors for hypertension. OBJECTIVE We constructed a sex-specific prediction model using Korean datasets, which included socioeconomic status, medical history, lifestyle-related variables, anthropometric status, and laboratory indices. METHODS We utilized the data from the Korea National Health and Nutrition Examination Survey from 2011 to 2015 to derive a hypertension prediction model. Participants aged 40 years or older. We constructed a sex-specific hypertension classification model using logistic regression and features obtained by literature review and statistical analysis. RESULTS We constructed a sex-specific hypertension classification model including approximately 20 variables. We estimated its performance using the Korea National Health and Nutrition Examination Survey dataset from 2016 to 2018 (AUC = 0.807 in men, AUC = 0.854 in women). The performance of our hypertension model was considered significant based on the cumulative incidence calculated from a longitudinal dataset, the Korean Genome and Epidemiology Study dataset. CONCLUSIONS We developed this hypertension prediction model using features that could be collected in a clinical office without difficulty. Individualized results may alert a person at high risk to modify unhealthy lifestyles.

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