Feasibility of Intelligent Models for Prediction of Utilization Factor of TBM

Noori, Alireza Mohammadi; Mikaeil, Reza; Mokhtarian, Mojtaba; Haghshenas, Sina Shaffiee; Foroughi, Mohammad Reza

doi:10.1007/s10706-020-01213-9

Cited by 49 publications

(18 citation statements)

References 61 publications

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“…Initially, evaluating the parametric correlation of each independent input dataset is necessary for carrying out reliable modeling [70][71][72][73]. Hence, before modeling, a correlation analysis was conducted using Pearson's correlation coefficient among the dataset for five independent input data, including maximum, minimum, and average temperature, relative humidity, and wind speed.…”

Section: Binary Classification Modelling Using Gmdhmentioning

confidence: 99%

Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis

et al. 2020

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Nowadays, sustainable development is considered a key concept and solution in creating a promising and prosperous future for human societies. Nevertheless, there are some predicted and unpredicted problems that epidemic diseases are real and complex problems. Hence, in this research work, a serious challenge in the sustainable development process was investigated using the classification of confirmed cases of COVID-19 (new version of Coronavirus) as one of the epidemic diseases. Hence, binary classification modeling was used by the group method of data handling (GMDH) type of neural network as one of the artificial intelligence methods. For this purpose, the Hubei province in China was selected as a case study to construct the proposed model, and some important factors, namely maximum, minimum, and average daily temperature, the density of a city, relative humidity, and wind speed, were considered as the input dataset, and the number of confirmed cases was selected as the output dataset for 30 days. The proposed binary classification model provides higher performance capacity in predicting the confirmed cases. In addition, regression analysis has been done and the trend of confirmed cases compared with the fluctuations of daily weather parameters (wind, humidity, and average temperature). The results demonstrated that the relative humidity and maximum daily temperature had the highest impact on the confirmed cases. The relative humidity in the main case study, with an average of 77.9%, affected positively, and maximum daily temperature, with an average of 15.4 • C, affected negatively, the confirmed cases.

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Section: Binary Classification Modelling Using Gmdhmentioning

confidence: 99%

Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis

et al. 2020

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“…By regression analysis, the effect of two or more variables on the dependent variable can be assessed. Multivariate linear regression (MLR) is one of the most applicable mathematical methods to determine a linear relationship between independent and dependent parameters [56,57]. The MLR model for the i th sample is as Equation (1):…”

Section: Mathematical Modeling and Multivariate Linear Regression Anamentioning

confidence: 99%

“…The highest beta coefficient means the maximum possible impact and can be used in a regression model to compare the relative importance of each coefficient. [56,57].…”

Section: Mathematical Modeling and Multivariate Linear Regression Anamentioning

confidence: 99%

Development of an Assessment Method for Investigating the Impact of Climate and Urban Parameters in Confirmed Cases of COVID-19: A New Challenge in Sustainable Development

Haghshenas

Pirouz

Haghshenas

et al. 2020

IJERPH

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Sustainable development has been a controversial global topic, and as a complex concept in recent years, it plays a key role in creating a favorable future for societies. Meanwhile, there are several problems in the process of implementing this approach, like epidemic diseases. Hence, in this study, the impact of climate and urban factors on confirmed cases of COVID-19 (a new type of coronavirus) with the trend and multivariate linear regression (MLR) has been investigated to propose a more accurate prediction model. For this propose, some important climate parameters, including daily average temperature, relative humidity, and wind speed, in addition to urban parameters such as population density, were considered, and their impacts on confirmed cases of COVID-19 were analyzed. The analysis was performed for three case studies in Italy, and the application of the proposed method has been investigated. The impacts of parameters have been considered with a delay time from one to nine days to find out the most suitable combination. The result of the analysis demonstrates the effectiveness of the proposed model and the impact of climate parameters on the trend of confirmed cases. The research hypothesis approved by the MLR model and the present assessment method could be applied by considering several variables that exhibit the exact delay of them to new confirmed cases of COVID-19.

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“…The artificial intelligence is one of the most dynamic research areas for researchers. This method has many applications in different scientific and industrial fields, including pattern recognition, control systems, as well as signal and image processing (Dormishi, Ataei, Mikaeil, Khalokakaei, & Haghshenas, 2019; Haghshenas et al, 2019; Mikaeil, Bakhshinezhad, Haghshenas, & Ataei, 2019; Mikaeil, Beigmohammadi, Bakhtavar, & Haghshenas, 2019; Mikaeil, Haghshenas, Haghshenas, & Ataei, 2018; Mikaeil, Haghshenas, & Hoseinie, 2018; Mikaeil, Haghshenas, & Sedaghati, 2019; Noori, Mikaeil, Mokhtarian, Haghshenas, & Foroughi, 2020; Rad, Haghshenas, & Haghshenas, 2014; Rad, Haghshenas, Kanafi, & Haghshenas, 2012; Salemi, Mikaeil, & Haghshenas, 2018; Shirani Faradonbeh, Shaffiee Haghshenas, Taheri, & Mikaeil, 2019). Artificial neural network is one of the most commonly used components in the area of artificial intelligence which has experienced a significant growth in recent decades from both practical and theoretical aspects.…”

Section: Group Methods Of Data Handling‐type Neural Networkmentioning

confidence: 99%

Implementation of an optimized binary classification by GMDH‐type neural network algorithm for predicting the blast produced ground vibration

2020

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Ground vibration is one of the most important undesired phenomena resulting from blasting operations imposing damages to facilities and buildings on the one hand, and creating environmental problems in open pit mining on the other. Therefore, the present study aims to provide an optimized classification binary model to identify the blasting patterns with an acceptable ground vibration intensity to reduce the damages resulting from this artificial phenomenon. This study uses a binary method to provide an optimized classification model for predicting and evaluating the blasting patterns with the minimum ground vibration. Group Method of Data Handling-Type Neural Network is used as one of the most practical optimization algorithms to solve complicated and uncertain problems in this modelling. In this study, by collecting the data of 52 different blasting patterns from Soungun copper mine, some of the most important geometric properties and the amount of ammonium nitrate fuel oil consumed in each blasting pattern are recorded. In addition, based on expertise and experience of experts, the degree of ground vibration produced by each blasting is qualitatively classified into four different ranges of very high, high, normal and low in the form of unacceptable (very high and High) and acceptable (normal and low) clusters. Based on the results obtained from the analyses, the developed model has a high flexibility and ability in the binary prediction of blasting patterns with an acceptable vibration magnitude.

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Feasibility of Intelligent Models for Prediction of Utilization Factor of TBM

Cited by 49 publications

References 61 publications

Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis

Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis

Development of an Assessment Method for Investigating the Impact of Climate and Urban Parameters in Confirmed Cases of COVID-19: A New Challenge in Sustainable Development

Implementation of an optimized binary classification by GMDH‐type neural network algorithm for predicting the blast produced ground vibration

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