中国矿业大学主页平台系统 zhengxigui--Home-- Predictive modeling of drilling rate index using machine learning approaches: LSTM, simple RNN, and RFA

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Supervisor of Doctorate Candidates

Supervisor of Master's Candidates

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School/Department:矿业工程学院

Administrative Position:采矿工程系系主任

Education Level:With Certificate of Graduation for Doctorate Study

Business Address:矿业学院B510-2

Gender:Male

Contact Information:13912041768

Degree:Doctor

Alma Mater:中国矿业大学

Discipline:Mining Engineering

Current position: Home > Scientific Research > Paper Publications

Predictive modeling of drilling rate index using machine learning approaches: LSTM, simple RNN, and RFA

Release time:2024-02-09 Hits:

DOI number:10.1080/10916466.2021.2003386

Affiliation of Author(s):矿业工程学院

Funded by:其他课题

First Author:Shahani, Niaz Muhammad

Co-author:Kamran, Muhammad,zhengxigui,liucancan

Indexed by:Journal paper

Document Code:5b5bb2948c6c69ce018c736a94c017e6

Volume:卷: 40期: 5页: 534-555

ISSN No.:1091-6466

Translation or Not:no

Date of Publication:2022-03-04