Real-time prediction of logging parameters during the drilling process using an attention-based Seq2Seq model

Zhang, Rui; Zhang, Chengkai; Song, Xianzhi; Li, Zukui; Su, Yinao; Li, Gensheng; Zhu, Zhaopeng

doi:10.1016/j.geoen.2023.212279

Cited by 9 publications

(1 citation statement)

References 40 publications

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“…The aim was to provide scientific reference and guidance for environmental monitoring and control solutions in the agricultural farming industry, further improving the intelligent level of rabbit shed environment monitoring and control, and achieving precise, efficient, and sustainable farming practices. Bert, Seq2seq, and transformer are the most common time series prediction models that have been widely applied in real-life scenarios, and many scholars have verified their predictive abilities with good results (Chen et al, 2022;Huang et al, 2023;Rosmaliati et al, 2023;Takeshi et al, 2022;Yang et al, 2022;Zhang et al, 2024). The Bert model is capable of capturing contextual information, but it has a large computational load.…”

Section: Experimental Settingsmentioning

confidence: 99%

Research on Multi-Parameter Prediction of Rabbit Housing Environment Based on Transformer

Liu,

Yang,

Zhang

et al. 2024

International Journal of Data Warehousing and Mining

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The rabbit breeding industry exhibits vast economic potential and growth opportunities. Nevertheless, the ineffective prediction of environmental conditions in rabbit houses often leads to the spread of infectious diseases, causing illness and death among rabbits. This paper presents a multi-parameter predictive model for environmental conditions such as temperature, humidity, illumination, CO2 concentration, NH3 concentration, and dust conditions in rabbit houses. The model adeptly distinguishes between day and night forecasts, thereby improving the adaptive adjustment of environmental data trends. Importantly, the model encapsulates multi-parameter environmental forecasting to heighten precision, given the high degree of interrelation among parameters. The model's performance is assessed through RMSE, MAE, and MAPE metrics, yielding values of 0.018, 0.031, and 6.31% respectively in predicting rabbit house environmental factors. Experimentally juxtaposed with Bert, Seq2seq, and conventional transformer models, the method demonstrates superior performance.

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