Principal component analysis-extreme learning machine (PCA-ELM) based coal mine water inrush prediction method

Abstract

The invention relates to a PCA-ELM based coal mine water inrush prediction method and belongs to the field of coal mine water inrush prediction methods. The method comprises the steps of obtaining a plurality of data affecting coal mine water inrush in the coal mine normal exploitation running state; screening a plurality of factors affecting coal mine water inrush through the PCA to obtain a main controlling factor playing a decisive role in coal mine water inrush; partitioning sample data only containing the main controlling factor into a training set, a verification set and a testing set which are corresponding to model training, verification and testing respectively; building an ELM network model, and training the data through the ELM algorithm; verifying a coal mine water inrush prediction model through the verification data, beginning to rebuild a model from the PCA if the obtaining prediction result has no apparent advantages compared with other algorithms, and using the model as the prediction model for actually predicting the coal mine water inrush prediction condition if the prediction result is ideal.

Description

technical field [0001] The invention relates to a coal mine water inrush prediction method, in particular to a coal mine water inrush prediction method based on PCA-EML. Background technique [0002] Coal mine water inrush is one of the major hidden dangers in coal mine safety production. Timely and correct prediction of water inrush is of great significance to ensure the smooth progress of coal mining. [0003] There are many factors affecting water inrush in coal mines. Most of these factors are uncertain and fuzzy, and there are complex nonlinear relationships among them. It is difficult to establish a prediction model with classical mathematical theories. Based on this, experts and scholars have proposed methods for predicting mine water inrush, such as using BP algorithm to establish a water inrush prediction neural network model based on mine water inrush sample examples, coal mine water inrush prediction method based on genetic neural network, and using support vector...

AI Technical Summary

Problems solved by technology

When the influencing factors of coal mine water inrush are used as network input variables, these influencing factors must contain information related to each other. Due to the non-independence between network input variables, information may overlap, which will increase the complexity of the network and reduce network performance. , affecting the prediction accuracy

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