YU Xiaoge，LIU Yifei，ZHAI Peihe. Identification of mine water inrush source based on PCA-AWOA-ELM model[J]. Coal Science and Technology，2023，51（3）：182−189
. DOI: 10.13199/j.cnki.cst.2021-0541| Citation: |
YU Xiaoge，LIU Yifei，ZHAI Peihe. Identification of mine water inrush source based on PCA-AWOA-ELM model[J]. Coal Science and Technology，2023，51（3）：182−189 . DOI: 10.13199/j.cnki.cst.2021-0541
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Mine water inrush is one of the most threatening disasters in the coal mine production process. In order to ensure safe coal mine production and improve the accuracy of mine water inrush source identification, a water source identification model based on improved whale optimization algorithm coupling extreme learning machine is proposed. Take Daizhuang Coal Mine as an example, choose Na+, Ca2+, Mg2+, Cl−, SO42−, HCO3− as the discrimination index, analyze and extract the main components of the evaluation index based on SPSS factor analysis. The correlation between the six ions is large, Ca2+ and Mg2+, Ca2+, Mg2+ and SO42−, Cl− has reached more than 0.7, and the correlation between SO42− and Cl− has also reached 0.68. Through principal component analysis, three principal components have been extracted, from six-dimensional space to three-dimensional space, while reducing the duplication of information between sample indicators. It also reduces the number of input layers of the limit learning machine and improves the generalization ability of the model for each type of data. Secondly, Secondly, the chaotic dynamic weight factor and the elite reverse mechanism are introduced to improve the whale algorithm. The improved whale optimization algorithm overcomes the disadvantage of random value of weight threshold of limit learning machine. The introduction of chaotic dynamic weight factor and elite reverse mechanism reduces the complexity of model calculation, improves the accuracy and speed of the algorithm, and jumps out of local optimization. Through training 38 groups of sample data, optimizing the weights and thresholds of the extreme learning machine, building a PCA-AWOA-ELM water source recognition model, and predicting 10 unknown test samples. The results show that the accuracy of the PCA-AWOA-ELM model is 100%, the accuracy of the PCA-WOA-ELM model is 90%, the accuracy of the PCA-ELM and ELM model is 60%, and the recognition accuracy, running speed and stability of the PCA-AWOA-ELM model are obvious. Higher than the PCA-WOA-ELM model, PCA-ELM model and ELM model, it provides an important guarantee for safe mine production.
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