FA-LSTM: A Novel Toxic Gas Concentration Prediction Model in Pollutant Environment

Ye, Cong; Zhao, Ximeng; Tang, Ke; Wang, Ge; Hu, Yifei; Jiao, Yingkui

doi:10.1109/access.2021.3133497

Cited by 16 publications

(4 citation statements)

References 26 publications

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“…At this stage, gas data monitoring technology has gradually matured, realising real-time online monitoring and real-time transmission and enabling online analysis and prediction based on various prediction models. A great deal of work has also been done in the field of gas prediction and early warning by domestic and international researchers through the use of algorithms, such as long short-term memory (LSTM), the autoregressive integrated moving average (ARIMA) model and support vector machines (SVMs) for gas concentration prediction [10][11][12][13][14]. An LSTM recurrent neural network prediction method is proposed for a gas concentration prediction analysis with prediction errors of 0.0005-0.04, demonstrating the applicability of the model for gas concentration prediction [15].…”

Section: Introductionmentioning

confidence: 99%

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

Huang

Fan

et al. 2023

Processes

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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.

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Section: Introductionmentioning

confidence: 99%

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

Huang

Fan

et al. 2023

Processes

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“…Peng et al [7] used sliding Lagrange interpolation to fill in missing values and then applied the ARIMA model to predict gas concentration in real time. Cong et al [8] used collected multidimensional gas data and environmental parameters as input features to obtain a dataset of gas data based on a sampling strategy for the construction of a feature-aware long short-term memory (LSTM) model to predict gas concentration. Dey et al [9] predicted the gas concentration by recovering the internal features of low-dimensional data and constructing a Bi-LSTM model.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Meng

et al. 2022

Sensors

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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.

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“…So, it successfully achieves the capture of long-term dependence of time series to a greater extent while retaining short-term information. Based on LSTM, researchers have done lots of research on improving the prediction accuracy [12][13][14][15][16]. Recently, the research of attention models in the field of computer vision has been attracting the attention of many researchers in other fields.…”

Section: Introductionmentioning

confidence: 99%

A Combined Model for Multivariate Time Series Forecasting Based on MLP-Feedforward Attention-LSTM

2022

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Multivariate time series forecasting has very great practical significance for a long time, and it has been attracting the attention of researchers from a diverse range of fields. However, it is difficult to analyze the relationship and transformation law among multivariate data. Further, it is hard to obtain a relatively accurate prediction. In recent years, long short-term memory (LSTM) has shown high capability in dealing with nonlinearity and long memory of time series data. Although LSTM can also process multivariate data, it is insufficient to pay various degrees of attention to multivariate data. To address this issue, a multivariate time series prediction model based on multilayer perceptron (MLP), feedforward attention mechanism, and LSTM is proposed in this paper. Firstly, the simulation process utilizes the MLP module to map the multivariate initial sequences into another latent dimensional space, thereby obtaining easily captured mapping features. Then, these features are adaptively assigned attention weights through the feed-forward attention mechanism. Finally, the LSTM module uses these feature sequences with attention weights to make final predictions. The experimental results show that the method that combines the MLP layer with the feed-forward attention layer is effective in extracting multivariate features. Also, the empirical results indicate that our proposed framework (a combined model of MLP-Feedforward attention-LSTM) can achieve better performance than baselines.INDEX TERMS Multivariate time series, Multilayer perceptron, Feed-forward attention mechanism, Long short-term memory network

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FA-LSTM: A Novel Toxic Gas Concentration Prediction Model in Pollutant Environment

Cited by 16 publications

References 26 publications

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

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

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

A Combined Model for Multivariate Time Series Forecasting Based on MLP-Feedforward Attention-LSTM

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