Predicting Calorific Value of Thar Lignite Deposit: A Comparison between Back-propagation Neural Networks (BPNN), Gradient Boosting Trees (GBT), and Multiple Linear Regression (MLR)-Reference-Cited by-同舟云学术

Predicting Calorific Value of Thar Lignite Deposit: A Comparison between Back-propagation Neural Networks (BPNN), Gradient Boosting Trees (GBT), and Multiple Linear Regression (MLR)

Published:2020-09-27 Issue:14 Volume:34 Page:1124-1136
ISSN:0883-9514
Container-title:Applied Artificial Intelligence
language:en
Short-container-title:Applied Artificial Intelligence

Author:

Ahmed Waqas¹,Muhammad Khan¹²^ORCID,Siddiqui Fahad Irfan³

Affiliation:

1. University of Engineering and Technology (UET), Peshawar, Pakistan

2. Department of Mining Engineering, University of Engineering and Technology Peshawar, Pakistan and Principal Investigator, Intelligent Information Processing Lab, National Centre of AI, UET Peshawar, Pakistan

3. Department of Mining Engineering, Mehran University of Engineering and Technology Jamshoro, Pakistan

Publisher

Informa UK Limited

Subject

Artificial Intelligence

Link

https://www.tandfonline.com/doi/pdf/10.1080/08839514.2020.1824091

Reference37 articles.

1. State-of-the-art in artificial neural network applications: A survey

2. Prediction of gross calorific value of coal based on proximate analysis using multiple linear regression and artificial neural networks

3. Linear regression-based correlations for estimation of high heating values of Pakistani lignite coals

4. Proximate analysis based multiple regression models for higher heating value estimation of low rank coals

5. Application of Artificial Neural Networks in Prediction of Uniaxial Compressive Strength of Rocks using Well Logs and Drilling Data

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