基于集成学习强化BPNN的掘进工作面温度预测模型

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中图分类号：TD727 文献标志码：A

Abstract： In view of the problems of existing tunneling face temperature prediction methods，such as weak generalization ability，poor robustness，and limited predictive capacity for nonlinear multidimensional data，a tunneling face temperature prediction model based on ensemble learning-enhanced Back Propagation Neural Network （BPNN），namely t-SNE-BPNN-AdaBoost，was proposed.First，the t-Distributed Stochastic Neighbor Embedding （t-SNE） nonlinear dimensionalityreduction technique was adopted to reduce seven high-dimensional features，includingair volume，temperature，andrelative humidityin frontof the ventilator，to three dimensions， retaining the local structure of data and removing noise.Then，the reduced-dimensional data were input into BPNNas the baseclassifier，andthe preliminarymodel wasobtained through iterative training.Finally，ensemble learning was carried out by Adaptive Boosting （AdaBoost）， in which multiple weak BPNN classfiers were iteratively trained and combined into a strong clasifier byweighted integration，thereby enhancing the generalization ability of the model. Sixty sets of measured tunneling face data were divided into training and testing sets at a ratio of8：2，and 5-fold cross-validation was conducted to determine that the optimal number of AdaBoost weak learners was 30. The experimental results showed that： ① the prediction curve of t-SNE-BPNNAdaBoost fit the true values best， with the smalest overalleror， strong adaptability in sudden temperature change intervals， and stability far superior to Support Vector Machine （SVM）， BPNN， and t-SNE-BPNN. ② The relative prediction error of t-SNE-BPNN-AdaBoost was the smallest， almost all within 5% ， demonstrating the best prediction accuracy. ③ On the test set， the coefficient of determination of t-SNE-BPNN-AdaBoost was 0.978 4， which was improved by 60.3% ， 17.2% ，and 8.1% compared with SVM， BPNN， and t-SNE-BPNN， respectively. The Mean Absolute Error （MAE） was 0.1676 ，the Mean Squared Error （MSE） was O.0567，and the Mean Absolute Percentage Error （MAPE） was 0.9640. Allmetrics were significantly better than those of SVM，BPNN， and t-SNE-BPNN，and the adaptability in sudden temperature change intervals was stronger.

Key words： tunneling face temperature prediction; t-Distributed Stochastic Neighbor Embedding; BP neural network; t-SNE; Adaptive Boosting; AdaBoost ensemble learning; 5-fold cross-validation

0 引言

随着矿山开采深度不断增加，井下掘进工作面环境日趋复杂，高温不仅威胁矿工健康和安全，还会影响设备运行效率，甚至诱发煤自燃、岩爆等次生灾害。（剩余12804字）