Machine learning algorithms play an important role in the detection of toxic, flammable and explosive gases, and they are extremely important for the study of mixed gas classification and concentration prediction methods. To solve the problem of low prediction accuracy of gas concentration regression prediction algorithms, a gas concentration prediction algorithm based on a stacking model is proposed in the current research. In this paper, the stochastic forest, extreme random regression tree and gradient boosting decision tree (GBDT) regression algorithms are selected as the base learning devices and use the stacking algorithm to take the output of each base learning device as input to train a new model to produce a final output. Through the stacking model, the grid search algorithm is studied to automatically optimize the parameters so that the performance of the entire system can reach the optimal parameters. Through experimental simulation, the gas concentration prediction algorithm based on stacking model has better prediction effect than other integrated frame algorithms and the accuracy of mixed gas concentration prediction is improved.

中文翻译：

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叠加技术的瓦斯浓度预测算法研究

机器学习算法在检测有毒，易燃气体和爆炸性气体中起着重要作用，对于混合气体分类和浓度预测方法的研究非常重要。为解决气体浓度回归预测算法预测精度低的问题，提出了一种基于叠加模型的气体浓度预测算法。本文选择随机森林，极端随机回归树和梯度提升决策树（GBDT）回归算法作为基础学习设备，并使用堆叠算法将每个基础学习设备的输出作为输入来训练新模型产生最终输出。通过堆叠模型，对网格搜索算法进行了研究，以自动优化参数，从而使整个系统的性能达到最佳参数。通过实验仿真，基于叠加模型的瓦斯浓度预测算法具有比其他集成框架算法更好的预测效果，提高了混合气体浓度预测的准确性。