Abstract
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.
Highlights
Mine slope deformation monitoring and prediction are very important for mine safety management and decision-making
It can be seen that quantum-behaved particle swarm optimization (QPSO)-Support Vector Machine (SVM) has the higher prediction precision than other models
From the prediction results comparison between SVM and other models, it can be seen that the Genetic algorithm (GA), particle swarm optimization (PSO), and QPSO optimization algorithm are necessary in improving network structure parameters
Summary
Mine slope deformation monitoring and prediction are very important for mine safety management and decision-making. Mine slope disaster predictions are extremely difficult because there are many kinds of disasters and disastrous mechanisms. Mine slope deformation forecast can analyze the extent of the disaster by small deformation changes and is easy to predict.[1] By consulting literature and field research, the deformation forecast considers that various factors such as geologic structure, hydro-geology, and atmosphere are relatively few at present.[2]. As a kind of natural geological disasters, landslide has caused huge loss of lives and properties.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
