Multivariate regression and genetic programming for prediction of backbreak in open-pit blasting

Sharma, Mukul; Agrawal, Hemant; Choudhary, Bhanwar Singh

doi:10.1007/s00521-021-06553-y

Cited by 26 publications

(6 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When all the variables introduced into the equation have reached a significant level, and at the same time, no new variables can be introduced, the stepwise regression is announced, and the optimal equation obtained at this time is between the least independent variables and the best fitting effect. The optimal equilibrium is reached [ 20 ]. It should be pointed out that the reason why it is emphasized here that the independent variables included in the regression equation should be as few as possible without affecting the fitting effect of the regression equation is that the more independent variables, the higher the requirements for data sources [ 21 ].…”

Section: Our Modelmentioning

confidence: 99%

Prediction and Analysis of Dynamic Changes of College Students’ Ideological and Political Changes Based on Multiple Regression

Wang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The construction of a harmonious society requires college students to coordinate their ideological, political, and moral qualities with social development and the needs of the times. Through the investigation and analysis of the ideological, political, and moral qualities of college students, on the one hand, we can see that the ideological, political, and moral qualities of college students are generally positive and healthy. On the other hand, it also exposes the outstanding problems in the ideological and political aspects of college students and the shortcomings of the ideological and political work in colleges and universities. This paper analyzes the dynamic changes of college students’ ideological and political changes and further studies the relationship between various indicators and students’ ideological and moral qualities through multiple linear regression analysis.

show abstract

Section: Our Modelmentioning

confidence: 99%

Prediction and Analysis of Dynamic Changes of College Students’ Ideological and Political Changes Based on Multiple Regression

Wang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The effect of input variables on the output variable was studied by sensitivity analysis using the cosine amplitude method in this study, enabling the identification of the most critical input variable. The mathematical relation is shown below. , R i j = ∑ k = 1 n ( x i k × x j k ) ∑ k = 1 n x i k 2 × ∑ k = 1 n x j k 2 where R ij is the sensitivity of an input variable, x i is the input variable, x j is the output variable, and n represents the number of data set.…”

Section: Discussionmentioning

confidence: 99%

Development of Empirical and Artificial Neural Network Model for the Prediction of Sorption Time to Assess the Potential of CO₂ Sequestration in Coal

et al. 2023

Self Cite

View full text Add to dashboard Cite

Geological sequestration of CO 2 in a coal seam is considered an attractive option to reduce the carbon footprint. It has an additional advantage of enhancing the recovery of coalbed methane, which has less sorption affinity toward coal in comparison to CO 2 . Desorption of gases from coal is controlled by various parameters, including reservoir depth and coal rank. A representative factor for desorption and diffusion in coal is the sorption time. It is an indicator which helps in estimation and evaluation of gas movement in the coal seam. Coals exhibiting high sorption time allow greater quantities of CO 2 injection and hold potential for CO 2 sequestration. Therefore, reliable and cost-effective estimation of sorption time is very important prior to investment in projects related to CO 2 sequestration. Generally, proximate and gas content analyses are part of the preliminary analysis of coal for the assessment of its potential as a coal-bed methane reservoir. In this study, data generated using these analyses were found very useful for estimating the sorption time and CO 2 sequestration potential of coal. The coal samples were collected from different depths of the Mand Raigarh coalfield for testing, and an empirical equation and artificial neural network (ANN)-based model have been developed to predict the sorption time of coal. The developed empirical equation predicts the sorption time with a coefficient of determination value of 0.88 and a root mean squared error value of ±1.07 days. Furthermore, the developed ANN model has been found to be very efficient in prediction with a correlation coefficient value of 0.97.

show abstract

“…Multiple regression analysis has a wider range of applications. Since linear regression analysis is relatively simple and common, the following first introduces multiple linear regression [25,26]. On the basis of linear analysis, dummy variable regression and a class of curve regression models that can be transformed into linear regression are gradually introduced.…”

Section: Cost Control Measures At the Completion Stagementioning

confidence: 99%

Cost Management and Forecasting Method for the Whole Life Cycle of Water Conservancy Projects Based on Multiple Regression Analysis

2022

Scientific Programming

View full text Add to dashboard Cite

The water conservancy project is an important livelihood engineering project, and it is also an important factor to ensure the living water supply, power supply stability, and economic development of the regional population. Therefore, the management of water conservancy projects is particularly important. The core management content is cost control, which can maximize the benefits of the project and avoid the waste of investors’ funds. This study studies the whole process control of water conservancy project cost, explains the problems existing in the current domestic water conservancy project cost control, and finally expounds on the whole process control measures, in order to provide a reference for related work. In this study, SPSS software is mainly used to establish a multiple linear regression model to analyze the influencing factors of water conservancy projects. Through the method of model solving, the relationship between hydraulic engineering and various variables is analyzed, and the multi-linear verification is improved, so as to further test the model, optimize the model, and finally manage and predict it accurately.

show abstract

Multivariate regression and genetic programming for prediction of backbreak in open-pit blasting

Cited by 26 publications

References 32 publications

Prediction and Analysis of Dynamic Changes of College Students’ Ideological and Political Changes Based on Multiple Regression

Prediction and Analysis of Dynamic Changes of College Students’ Ideological and Political Changes Based on Multiple Regression

Development of Empirical and Artificial Neural Network Model for the Prediction of Sorption Time to Assess the Potential of CO₂ Sequestration in Coal

Cost Management and Forecasting Method for the Whole Life Cycle of Water Conservancy Projects Based on Multiple Regression Analysis

Contact Info

Product

Resources

About

Multivariate regression and genetic programming for prediction of backbreak in open-pit blasting

Cited by 26 publications

References 32 publications

Prediction and Analysis of Dynamic Changes of College Students’ Ideological and Political Changes Based on Multiple Regression

Prediction and Analysis of Dynamic Changes of College Students’ Ideological and Political Changes Based on Multiple Regression

Development of Empirical and Artificial Neural Network Model for the Prediction of Sorption Time to Assess the Potential of CO2 Sequestration in Coal

Cost Management and Forecasting Method for the Whole Life Cycle of Water Conservancy Projects Based on Multiple Regression Analysis

Contact Info

Product

Resources

About

Development of Empirical and Artificial Neural Network Model for the Prediction of Sorption Time to Assess the Potential of CO₂ Sequestration in Coal