A rapid identification model of mine water inrush based on extreme learning machine

In the process of disaster prevention of coal mine water inrush,it is necessary to quickly and accurately identify the types of water sources. The technology of laser induced fluorescence has the characteristics of high sensi- tivity,rapid and accurate for monitoring,and it also obtains the fluorescence spectra of water samples. After preprocess- ing spectra with Savitzky-Golay algorithm and feature extraction with principal component analysis,the multi-classifica- tion learning model is established by the extreme learning machine algorithm. The Sigmoid function is determined as hidden layer activation function,and the optimal number of hidden layer nodes is determined through the cross valida- tion method. From the average time of training network,the average accuracy of classification and the standard devia- tion of accuracies,the performance is compared with the conventional classification algorithms such as BP and SVM. The results show that the model is consistent with the conventional classification model on the average accuracy of clas- sification in the training and test set. While the standard deviation of accuracies is minimum,it shows that the model has the stable performance of classification. When training the model,the learning time is greatly reduced. Therefore, the model is more suitable for the rapid and accurate classification of water inrush sources.