Research on methane concentration prediction method based on non-steady-state feature coupling interaction and structural perception loss

As the core of pollution prevention and management, accurate PM2.5 concentration prediction is crucial for human survival. However, due to the nonstationarity and nonlinearity of PM2.5 concentration data, the accurate prediction for PM2.5 concentration remains a challenge. In this study, a PM2.5 concentration prediction method using weighted complementary ensemble empirical mode decomposition with adaptive noise (WCEEMDAN) and improved long and short-term memory (ILSTM) neural network is proposed. Firstly, a novel WCEEMDAN method is proposed to correctly identify the non-stationary and non-linear characteristics and divide the PM2.5 sequences into various layers. Through the correlation analysis with PM2.5 data, these sub-layers are given different weights. Secondly, the adaptive mutation particle swarm optimization (AMPSO) algorithm is developed to obtain the main hyperparameters of the long short-term memory network (LSTM) neural network, improving the prediction accuracy of PM2.5 concentration. The optimization convergence speed and accuracy are improved by adjusting the inertia weight and introducing the mutation mechanism to enhance the global optimization ability. Finally, three groups of PM2.5 concentration data are utilized to verify the effectiveness of the proposed model. Compared with other methods, the experimental results demonstrate the superiority of the proposed model. The source code can be downloaded from https://github.com/zhangli190227/WCEENDAM-ILSTM .

The article applied neural net-work analysis method in prediction of coal-bed methane content. And use this method to analysis the main prediction factors of coal-bed methane content. And then the paper based on the neural network built the mathematics prediction models and system structure of coal-bed methane content. At last we applied this model in prediction of coal-bed methane content in Qinshui coal mine. Projections show that use neural network to predict the methane content of coal-bed is feasible.

PM_2.5 has a non-negligible impact on visibility and air quality as an important component of haze and can affect cloud formation and rainfall and thus change the climate, and it is an evaluation indicator of air pollution level. Achieving PM_2.5 concentration prediction based on relevant historical data mining can effectively improve air pollution forecasting ability and guide air pollution prevention and control. The past methods neglected the impact caused by PM_2.5 flow between cities when analyzing the impact of inter-city PM_2.5 concentrations, making it difficult to further improve the prediction accuracy. However, factors including geographical information such as altitude and distance and meteorological information such as wind speed and wind direction affect the flow of PM_2.5 between cities, leading to the change of PM_2.5 concentration in cities. So a PM_2.5 directed flow graph is constructed in this paper. Geographic and meteorological data is introduced into the graph structure to simulate the spatial PM_2.5 flow transmission relationship between cities. The introduction of meteorological factors like wind direction depicts the unequal flow relationship of PM_2.5 between cities. Based on this, a PM_2.5 concentration prediction method integrating spatial-temporal factors is proposed in this paper. A spatial feature extraction method based on weight aggregation graph attention network (WGAT) is proposed to extract the spatial correlation features of PM_2.5 in the flow graph, and a multi-step PM_2.5 prediction method based on attention gate control loop unit (AGRU) is proposed. The PM_2.5 concentration prediction model WGAT-AGRU with fused spatiotemporal features is constructed by combining the two methods to achieve multi-step PM_2.5 concentration prediction. Finally, accuracy and validity experiments are conducted on the KnowAir dataset, and the results show that the WGAT-AGRU model proposed in the paper has good performance in terms of prediction accuracy and validates the effectiveness of the model.

Urban air pollutant concentration prediction is dealing with a surge of massive environmental monitoring data and complex changes in air pollutants. This requires effective prediction methods to improve prediction accuracy and to prevent serious pollution incidents, thereby enhancing environmental management decision-making capacity. In this paper, a new pollutant concentration prediction method is proposed based on the vast amounts of environmental data and deep learning techniques. The proposed method integrates big data by using two kinds of deep networks. This method is based on the design that uses a convolutional neural network as the base layer, automatically extracting features of input data. A long short-term memory network is used for the output layer to consider the time dependence of pollutants. Our model consists of these two deep networks. With performance optimization, the model can predict future particulate matter (PM2.5) concentrations as a time series. Finally, the prediction results are compared with the results of numerical models. The applicability and advantages of the model are also analyzed. The experimental results show that it improves prediction performance compared with classic models.

Effectively avoiding methane accidents is vital to the security of manufacturing minerals. Coal mine methane accidents are often caused by a methane concentration overrun, and accurately predicting methane emission quantity in a coal mine is key to solving this problem. To maintain the concentration of methane in a secure range, grey theory and neural network model are increasingly used to critically forecasting methane emission quantity in coal mines. A limitation of the grey neural network model is that researchers have merely combined the conventional neural network and grey theory. To enhance the accuracy of prediction, a modified grey GM (1,1) and radial basis function (RBF) neural network model is proposed, which combines the amended grey GM (1,1) model and RBF neural network model. In this article, the proposed model is put into a simulation experiment, which is built based on Matlab software (MathWorks.Inc, Natick, Masezius, U.S). Ultimately, the conclusion of the simulation experiment verified that the modified grey GM (1,1) and RBF neural network model not only boosts the precision of prediction, but also restricts relative error in a minimum range. This shows that the modified grey GM (1,1) and RBF neural network model can make more effective and precise predict the predicts, compared to the grey GM (1,1) model and RBF neural network model.

Development of a concentration prediction method combined with wavelength selection and decorrelation methods for effective process monitoring in continuous processes

Response lag is an inevitable problem for most electrochemical gas sensors, while the information about gas concentration and trends should be obtained immediately in some specific fields. Therefore, focusing on the goal of improving the accuracy and time efficiency of gas concentration prediction, and adopting data-driven method, this paper proposes a novel regression prediction model—Denoising Deep Forest(DDF). Comparative experiment’s results show that Denoising Deep Forest’s MAPE value is 4.43% and R2 value is 0.991 on ethylene oxide gas leakage detection dataset, which outperforms other common models. Moreover, this paper designs a noise-influence experiment to further test the performance of the models. The results of noise-influence experiment indecate that compared with the common models, Denoising Deep Forest has stronger anti-noise ability and it can maintain the robust performance on data with various noises.

The widespread application of the drill-and-blast method in tunnel construction generates instantaneous high-concentration dust, posing severe threats to workers’ health and safety. However, existing contact-based monitoring techniques, such as filter membrane weighing and light-scattering sensors, are hindered by operational complexity, maintenance challenges, data latency, and an inability to capture whole-field distribution. To address these limitations, this study conducted in situ experiments to construct a dust image dataset containing rich “real-world noise”. Analysis revealed significant variations in visibility, contrast, and light-scattering intensity across different concentration levels, establishing a physical basis for visual feature extraction. Consequently, a dust concentration prediction method based on transfer learning and CNN was proposed. Comparative training of six network models, including ResNet50, demonstrated that VGG16 achieved the best performance with an R2 of 0.9684, verifying the method’s feasibility. Furthermore, hyperparameter optimization (learning rate: 0.001; batch size: 32; dataset split: 8:1:1) was shown to further enhance prediction accuracy.

Quantitative detection of polluted gas by electronic nose can reduce the cost of detection and improve the efficiency of measurement. Through the effective pattern recognition method, the electronic nose can analyse the continuous periodic data and realise the detection of specific tasks. In this paper, the pollution gas concentration prediction method based on bidirectional long-short term memory network (Bi-LSTM) is proposed. And the effect of the Bi-LSTM model with different time steps, hidden layers and different combinations of sensor features on the performance of pollution gas prediction model is investigated. This method can extract deep features by automatically learning the gas response information of the sensor array, and its performance is better. The proposed method is verified on the air quality dataset, which proves that the proposed method has high accuracy in the quantitative detection of gas concentration based on electronic nose information.

To more effectively capture the periodic and dynamic changes in urban traffic flow and the spatiotemporal correlation of complex road networks, a new traffic flow prediction method, the Enhanced Spatiotemporal Graph Convolutional Network Encoder-Decoder Model (ESGCN-EDM), is proposed. The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. The aPFGCN module effectively reduces the dimensionality of features and decreases model complexity to obtain the final node feature representation by personalizing the adjustment of node influence coefficients and applying Fourier transform and inverse transform techniques. Additionally, by incorporating an attention mechanism, it enhances the model’s ability to focus on important information and effectively captures the spatial topological relationships within the traffic network. The TCBiL module integrates 1D convolution with BiLSTM to form a unified temporal feature extraction module. The 1D convolution is utilized to extract local features from the time series, while the BiLSTM captures long-term dependencies within the time series. This allows for simultaneous feature extraction and temporal modeling, thereby enhancing the model’s efficiency and performance, and strengthening its ability to model time series. In the encoder part of the ESGCN-EDM, the aPFGCN is combined with the TCBiL to handle the spatiotemporal coupling interactions of the road network. The decoder part then performs multi-step predictions based on spatiotemporal sequences using the TCBiL and CNN, generating high-dimensional representations. Extensive experiments conducted on two real-world road traffic datasets demonstrate that the ESGCN-EDM model consistently outperforms other benchmark models in 1-hour, 30-minute, and 15-minute traffic flow predictions. Specifically, on the PeMSD8 dataset, the model achieves reductions in MAE, RMSE, and SMAPE by 7.9%, 2.1%, and 16.9%, respectively, compared to the AMRGCN model for 1-hour predictions. Similarly, on the PeMSD4 dataset, the model reduces MAE, RMSE, and MAPE by 1.8%, 1.1%, and 3.0%, respectively. These results validate the efficacy of the proposed model and its ability to significantly enhance the accuracy of traffic flow forecasting.

Owing to the limitations of testing methods and the inaccuracy of knowledge, the obtained power transformer information is characterized by fuzziness and randomness, which leads to a lack of accurate description of transformer internal coupling interaction and fault evolution in operation and maintenance departments. Therefore, a thermal state parameter prediction method for a 110 kV transformer based on multi-modal deep learning is designed. The data were resampled and smoothed, and high-frequency noise was removed using a wavelet transform. The running time and spatial temperature rise characteristics of the thermal state parameters of the 110 kV transformer were analyzed, and the variational mode decomposition method was used to decompose the thermal state parameters. By constructing a multimodal deep learning network, abstracted relevant features were extracted from high-dimensional complex input data. By constructing the state equation and measurement equation of the hot spot temperature, a real-time optimal estimation model of the hot spot temperature was established. The extended Kalman filter method was used to build a prediction model for a nonlinear top oil temperature rise system. The experimental results showed that the deviation from the actual value was the least, and the difference between the predicted and measured values of the top oil temperature was relatively small. The prediction time was lower than 0.2s, and the remaining life prediction was correct for 17 units.