ArticleSensors (Basel, Switzerland)2023
Combined Prediction Model of Gas Concentration Based on Indicators Dynamic Optimization and Bi-LSTMs.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Sensor-Based Ozone Monitoring and Forecasting in a Synchrotron Radiation Laboratory Using Autoregressive Integrated Moving Average Models.Sensors (Basel, Switzerland) · 2026Article
- Integrating Artificial Intelligence into Ventilation on Demand: Current Practice and Future Promises.Sensors (Basel, Switzerland) · 2026Review
- A lightweight intelligent model for VOC mixture analysis: toward preclinical breath biomarker analysis.Frontiers in bioengineering and biotechnology · 2026Article
- Generating Low-Earth Orbit Satellite Attitude Maneuver Profiles Using Deep Neural Networks.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
In order to accurately predict the gas concentration, find out the gas abnormal emission in advance, and take effective measures to reduce the gas concentration in time, this paper analyzes multivariate monitoring data and proposes a new dynamic combined prediction method of gas concentration. Spearman's rank correlation coefficient is applied for the dynamic optimization of prediction indicators. The time series and spatial topology features of the optimized indicators are extracted and input into the combined prediction model of gas concentration based on indicators dynamic optimization and Bi-LSTMs (Bi-directional Long Short-term Memory), which can predict the gas concentration for the next 30 min. The results show that the other gas concentration, temperature, and humidity indicators are strongly correlated with the gas concentration to be predicted, and Spearman's rank correlation coefficient is up to 0.92 at most. The average
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Registered trials
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