TY - GEN
T1 - An Optimized Stacking Ensemble Learning Model Using 3-Pyramids Technique for the 2006 CHF Groeneveld Look Table Prediction
AU - Djeddou, Messaoud
AU - Dallal, Jehad Al
AU - Hellal, Aouatef
AU - Hameed, Ibrahim A.
AU - Loukam, I.
AU - Kabir, Md F.
AU - Bouhicha, M.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Typically, critical heat flux (CHF) look-up tables constructed based on physical experiments have limited data. Using available experimental-based CHF look-up tables as training data, machine learning techniques can be applied to construct CHF prediction models and, thus, produce CHF look-up tables reporting estimated CHF values under a much wider variety of conditions. This study proposes an effective prediction approach based on stacking ensemble learning and a new optimization technique, namely 3-pyramids, to get reliable CHF look-up prediction results. The approach is divided into three parts that include (1) using multiple prediction models constructed using different machine learning techniques, (2) combining the prediction results using a stacking strategy to generate the final prediction results, and (3) optimizing an improved super learner model to improve the prediction model's capabilities using 3-pyramids optimization technique. The effectiveness of these models was evaluated using the coefficient of determination (R2), mean absolute error (MAE), and root-mean-squared (RMSE). The results show that the proposed model has high accuracy and that applying the 3-pyramids optimization further improved this accuracy and significantly reduced the model execution time.
AB - Typically, critical heat flux (CHF) look-up tables constructed based on physical experiments have limited data. Using available experimental-based CHF look-up tables as training data, machine learning techniques can be applied to construct CHF prediction models and, thus, produce CHF look-up tables reporting estimated CHF values under a much wider variety of conditions. This study proposes an effective prediction approach based on stacking ensemble learning and a new optimization technique, namely 3-pyramids, to get reliable CHF look-up prediction results. The approach is divided into three parts that include (1) using multiple prediction models constructed using different machine learning techniques, (2) combining the prediction results using a stacking strategy to generate the final prediction results, and (3) optimizing an improved super learner model to improve the prediction model's capabilities using 3-pyramids optimization technique. The effectiveness of these models was evaluated using the coefficient of determination (R2), mean absolute error (MAE), and root-mean-squared (RMSE). The results show that the proposed model has high accuracy and that applying the 3-pyramids optimization further improved this accuracy and significantly reduced the model execution time.
UR - http://www.scopus.com/inward/record.url?scp=85186760733&partnerID=8YFLogxK
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U2 - 10.1109/ICAMCS59110.2023.00022
DO - 10.1109/ICAMCS59110.2023.00022
M3 - Conference contribution
AN - SCOPUS:85186760733
T3 - Proceedings - 2023 International Conference on Applied Mathematics and Computer Science, ICAMCS 2023
SP - 95
EP - 99
BT - Proceedings - 2023 International Conference on Applied Mathematics and Computer Science, ICAMCS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Applied Mathematics and Computer Science, ICAMCS 2023
Y2 - 8 August 2023 through 10 August 2023
ER -