Research on a Gas Concentration Prediction Algorithm Based on Stacking

A Spark Streaming-Based Early Warning Model for Gas Concentration Prediction

The prediction and early warning efficiency of mine gas concentrations are important for intelligent monitoring of daily gas concentrations in coal mines. It is used as an important means for ensuring the safe and stable operation of coal mines. This study proposes an early warning model for gas concentration prediction involving the Spark Streaming framework (SSF). The model incorporates a particle swarm optimisation algorithm (PSO) and a gated recurrent unit (GRU) model in the SSF, and further experimental analysis is carried out on the basis of optimising the model parameters. The operational efficiency of the model is validated using a control variable approach, and the prediction and warning errors is verified using MAE, RMSE and R2. The results show that the model is able to predict and warn of the gas concentration with high efficiency and high accuracy. It also features fast data processing and fault tolerance, which provides a new idea to continue improving the gas concentration prediction and warning efficiency and some theoretical and technical support for intelligent gas monitoring in coal mines.

A Gas Concentration Prediction Method Driven by a Spark Streaming Framework

In the traditional coal-mine gas-concentration prediction process, problems such as low timeliness of data and low efficiency of the prediction model in learning data features result in low accuracy of the final prediction. To solve these problems, a gas-concentration prediction method driven by the Spark Streaming framework is proposed. In this research study, the Spark Streaming framework, autoregressive integrated moving average (ARIMA) model and support vector machine (SVM) model are used to construct a new prediction model called the SPARS model. The Spark Streaming framework is used to process large batches of real-time streaming data in a short period of time, and the model can be used to intermittently update and optimize the prediction model so that the model can fully learn the characteristics of the data. At the same time, the advantages of the ARIMA model and SVM model for processing linear data and nonlinear data are combined to improve the model’s prediction efficiency and fully reflect the timeliness of gas prediction. Finally, the proposed prediction model is verified using gas data collected on site. The optimal learning time for the SPARS model in predicting this set of data is determined, and a comparative analysis of the prediction results obtained from the ARIMA, SVM and other models fully confirms that high-precision prediction results can be obtained using the SPARS model. The proposed model can be used to realize scientific and accurate real-time prediction and analyses of coal-mine gas concentrations and provides a new idea for realizing real-time and accurate gas prediction in coal mines.

Gas Concentration Prediction Based on IWOA-LSTM-CEEMDAN Residual Correction Model

In this study, to further improve the prediction accuracy of coal mine gas concentration and thereby preventing gas accidents and improving coal mine safety management, the standard whale optimisation algorithm’s (WOA) susceptibility to falling into local optima, slow convergence speed, and low prediction accuracy of the single-factor long short-term memory (LSTM) neural network residual correction model are addressed. A new IWOA-LSTM-CEEMDAN model is constructed based on the improved whale optimisation algorithm (IWOA) to improve the IWOA-LSTM one-factor residual correction model through the use of the complete ensemble empirical model decomposition with adaptive noise (CEEMDAN) method. The population diversity of the WOA is enhanced through multiple strategies and its ability to exit local optima and perform global search is improved. In addition, the optimal weight combination model for subsequence is determined by analysing the prediction error of the intrinsic mode function (IMF) of the residual sequence. The experimental results show that the prediction accuracy of the IWOA-LSTM-CEEMDAN model is higher than that of the BP neural network and the GRU, LSTM, WOA-LSTM, and IWOA-LSTM residual correction models by 47.48%, 36.48%, 30.71%, 27.38%, and 12.96%, respectively. The IWOA-LSTM-CEEMDAN model also achieves the highest prediction accuracy in multi-step prediction.

An Ensemble Method for Missing Data of Environmental Sensor Considering Univariate and Multivariate Characteristics

With rapid urbanization, awareness of environmental pollution is growing rapidly and, accordingly, interest in environmental sensors that measure atmospheric and indoor air quality is increasing. Since these IoT-based environmental sensors are sensitive and value reliability, it is essential to deal with missing values, which are one of the causes of reliability problems. Characteristics that can be used to impute missing values in environmental sensors are the time dependency of single variables and the correlation between multivariate variables. However, in the existing method of imputing missing values, only one characteristic has been used and there has been no case where both characteristics were used. In this work, we introduced a new ensemble imputation method reflecting this. First, the cases in which missing values occur frequently were divided into four cases and were generated into the experimental data: communication error (aperiodic, periodic), sensor error (rapid change, measurement range). To compare the existing method with the proposed method, five methods of univariate imputation and five methods of multivariate imputation—both of which are widely used—were used as a single model to predict missing values for the four cases. The values predicted by a single model were applied to the ensemble method. Among the ensemble methods, the weighted average and stacking methods were used to derive the final predicted values and replace the missing values. Finally, the predicted values, substituted with the original data, were evaluated by a comparison between the mean absolute error (MAE) and the root mean square error (RMSE). The proposed ensemble method generally performed better than the single method. In addition, this method simultaneously considers the correlation between variables and time dependence, which are characteristics that must be considered in the environmental sensor. As a result, our proposed ensemble technique can contribute to the replacement of the missing values generated by environmental sensors, which can help to increase the reliability of environmental sensor data.