ArticleScientific reports2025
Research on coal and gas outburst risk prediction based on improved search algorithm optimized deep learning network.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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
1 citing paper in PubMed.
- Intelligent monitoring and CNN-based performance evaluation of borehole-pipe-pump gas drainage systems in coal mines.Scientific reports · 2026Article
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Authors and funding
1 author.
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Abstract
With the gradual deepening of coal mining, the surrounding rock pressure significantly increases, and the risk of gas release and accumulation also increases, increasing the likelihood of coal and gas outburst hazards. This study used boxplot and data interpolation method to preprocess data and used correlation to screen out highly correlated influencing factors as disaster prediction indicators. Build an initial prediction model framework using Convolutional Neural Network (CNN), optimize model hyperparameters using Chaos Mapping and Levy Flight Improved Crow Search Algorithm (ICSA), and establish a coal and gas outburst prediction model based on ICSA-CNN. Finally, a comparative model was established to compare the evaluation indicators and confusion matrix. According to the results, the ICSA-CNN model stood out as the most accurate in its predictive capabilities, better robustness and generalization ability, and higher security.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.