A Novel Deformation Prediction Model for Mine Slope Surface Using Meteorological Factors Based on Kernel Extreme Learning Machine

Du, Sun Wen; Zhang, Jin; Deng, Zeng Bing; Li, Jing Tao

doi:10.4028/www.scientific.net/jera.12.67

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…Various kinds of artificial intelligence algorithms are used to predict mine slope deformation based on the meteorological data, such as Artificial Neural Network (ANN), 22 GRNN, 23 and Extreme Learning Machine (ELM). 24 In the study of deformation prediction model, input factors selection and method of building ontology model are two keys. Hence, more suitable factors influencing the deformation of mine slope, more faster, and more accurate prediction model should be selected.…”

Section: Nmse =mentioning

confidence: 99%

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A novel deformation forecasting method utilizing comprehensive observation data

Yao

2018

Advances in Mechanical Engineering

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Mine disasters often happen unpredictably and it is necessary to find an effective deformation forecasting method. A model between deformation data and the factors data that affected deformation is built in this study. The factors contain hydro-geological factors and meteorological factors. Their relationship presents a complex nonlinear relationship which cannot be solved by ordinary methods such as multiple linear regression. With the development of artificial intelligence algorithm, Artificial Neural Network, Support Vector Machine, and Extreme Learning Machine come to the fore. Support Vector Machine could establish a deformation prediction model perfectly in the condition that there is less input data and output data. The deformation forecast model that uses quantum-behaved particle swarm optimization algorithm is selected to optimize the Support Vector Machine. The optimum configuration of Support Vector Machine model needs to be determined by two parameters, that is, normalized mean square error and correlation coefficient (R). Quantum-behaved particle swarm optimization could determine the optimal parameter values by minimizing normalized mean square error. It investigates the application effect of the proposed quantum-behaved particle swarm optimization-Support Vector Machine model by comparing their performances of popular forecasting models, such as Support Vector Machine, GA-Support Vector Machine, and particle swarm optimization-Support Vector Machine models. The results show that the proposed model has better performances in mine slope surface deformation and is superior to its rivals.

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Section: Nmse =mentioning

confidence: 99%

“…The forecasting model selection is also very important for convergence time and prediction accuracy. ANN, 22 GRNN, 23 and ELM 24 were used to study and build prediction models. However, each method has its disadvantages.…”

Section: Nmse =mentioning

confidence: 99%

A novel deformation forecasting method utilizing comprehensive observation data

Yao

2018

Advances in Mechanical Engineering

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“…Hu et al [19] used a machine-learning method to model slope deformation as a Gaussian Process. Du et al [20] established an ELM model for slope deformation estimation. Liu et al [21] proposed a neuron-fuzzy model for slope deformation estimation.…”

Section: Introductionmentioning

confidence: 99%

A New Machine-Learning Prediction Model for Slope Deformation of an Open-Pit Mine: An Evaluation of Field Data

Feng

Wang

et al. 2019

Energies

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Effective monitoring of the slope deformation of an open-pit mine is essential for preventing catastrophic collapses. It is a challenging task to accurately predict slope deformation. To this end, this article proposed a new machine-learning method for slope deformation prediction. Ground-based interferometric radar (GB-SAR) was employed to collect the slope deformation data from an open-pit mine. Then, an ensemble learner, which aggregated a set of weaker learners, was proposed to mine the GB-SAR field data, delivering a slope deformation prediction model. The evaluation of the field data acquired from the Anjialing open-pit mine demonstrates that the proposed slope deformation model was able to precisely predict the slope deformation of the monitored mine. The prediction accuracy of the super learner was superior to those of all the independent weaker learners.

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Elucidating the role of topological pattern discovery and support vector machine in generating predictive models for Indian summer monsoon rainfall

Chattopadhyay

Chattopadhyay²

2015

Theor Appl Climatol

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The present paper reports a study, where growing hierarchical self-organising map (GHSOM) has been applied to achieve a visual cluster analysis to the Indian rainfall dataset consisting of 142 years of Indian rainfall data so that the yearly rainfall can be segregated into small groups to visualise the pattern of clustering behaviour of yearly rainfall due to changes in monthly rainfall for each year. Also, through support vector machine (SVM), it has been observed that generation of clusters impacts positively on the prediction of the Indian summer monsoon rainfall. Results have been presented through statistical and graphical analyses.

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A Novel Deformation Prediction Model for Mine Slope Surface Using Meteorological Factors Based on Kernel Extreme Learning Machine

Cited by 7 publications

References 19 publications

A novel deformation forecasting method utilizing comprehensive observation data

A novel deformation forecasting method utilizing comprehensive observation data

A New Machine-Learning Prediction Model for Slope Deformation of an Open-Pit Mine: An Evaluation of Field Data

Elucidating the role of topological pattern discovery and support vector machine in generating predictive models for Indian summer monsoon rainfall

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