A novel combined intelligent algorithm prediction model for the risk of the coal and gas outburst.

ABSTRACT: The mechanism of coal and gas outburst disasters is perplexing, and the evaluation methods of outburst disasters based on various sensitive indicators often have some imprecision and fuzziness. With the concept of accurate and intelligent mining in coal mines proposed in China, selecting quantifiable parameters for machine learning risk prediction can avoid the deviation caused by human subjectivity, and improve the accuracy of coal and gas outburst prediction. Aiming at the shortcomings of the support vector machine (SVM) such as low noise resistance and being prone to be influenced by parameters easily, this research proposed a prediction method based on a grey wolf optimizer to optimize the support vector machine (GWO-SVM). To coordinate the global and local optimization ability of the

SUBMITTER: Wang Z 

PROVIDER: S-EPMC10520021 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature