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Neural Computing and Applications

Erschienen in:

09.11.2020 | S.I.: SPIoT 2020

Overflow warning and remote monitoring technology based on improved random forest

verfasst von: Haibo Liang, Haochen Han, Pengbo Ni, Yingjun Jiang

Erschienen in: Neural Computing and Applications | Ausgabe 9/2021

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Abstract

Overflow accident is one of the accidents with the greatest impact on safe drilling. An unclear diagnosis of an overflow accident or improper post-processing may cause serious drilling accidents. Based on this, a remote monitoring platform for overflow accidents is proposed. The diagnosis method of overflow accidents was researched and optimized, and a random forest overflow accident identification and classification model based on bat algorithm optimization was established. This model can optimize the optimal parameter combination and has the ability to handle a large number of eigenvalues. Through comparative analysis, the optimization model has higher accuracy, and the accuracy rate is increased by 6.67%.

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Titel: Overflow warning and remote monitoring technology based on improved random forest
verfasst von: Haibo Liang
Haochen Han
Pengbo Ni
Yingjun Jiang
Publikationsdatum: 09.11.2020
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 9/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05425-1

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