Advanced hybrid LSTM-transformer architecture for real-time multi-task prediction in engineering systems

In the field of engineering systems—particularly in underground drilling and green stormwater management—real-time predictions are vital for enhancing operational performance, ensuring safety, and increasing efficiency. Addressing this niche, our study introduces a novel LSTM-transformer hybrid arch...

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Bibliographic Details
Published in:	Scientific reports Vol. 14; no. 1; pp. 4890 - 24
Main Authors:	Cao, Kangjie, Zhang, Ting, Huang, Jueqiao
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 28.02.2024 Nature Publishing Group Nature Portfolio
Subjects:	639/166 639/705/1042 639/705/1046 639/705/117 Adaptability Computer applications Distillation Drilling Engineering Engineering systems Green stormwater management Humanities and Social Sciences LSTM-transformer hybrid architecture Multi-task learning multidisciplinary Prediction models Real-time predictions Science Science (multidisciplinary) Stormwater Stormwater management Underground drilling Real-time predictions Knowledge distillation LSTM-transformer hybrid architecture Underground drilling Predictive accuracy Multi-task learning Online learning Engineering systems Green stormwater management
ISSN:	2045-2322, 2045-2322
Online Access:	Get full text
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