2022
DOI: 10.3390/e24111674
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Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting

Abstract: The forecasting and prediction of crude oil are necessary in enabling governments to compile their economic plans. Artificial neural networks (ANN) have been widely used in different forecasting and prediction applications, including in the oil industry. The dendritic neural regression (DNR) model is an ANNs that has showed promising performance in time-series prediction. The DNR has the capability to deal with the nonlinear characteristics of historical data for time-series forecasting applications. However, … Show more

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Cited by 20 publications
(7 citation statements)
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“…19 Al-Qaness et al presented a metaheuristic optimisation algorithm for optimising the DNR model and used the model to forecast oil production. 20 Al-Qaness et al applied a modified Aquila optimiser with the opposition-based learning technique to forecast oil production. 21 These studies show that the hybrid model is not only based on the concept of decomposition-integration, but also combines the advantages of all models and compensates for the disadvantages of single and mixed single models.…”
Section: Literature Reviewmentioning
confidence: 99%
See 1 more Smart Citation
“…19 Al-Qaness et al presented a metaheuristic optimisation algorithm for optimising the DNR model and used the model to forecast oil production. 20 Al-Qaness et al applied a modified Aquila optimiser with the opposition-based learning technique to forecast oil production. 21 These studies show that the hybrid model is not only based on the concept of decomposition-integration, but also combines the advantages of all models and compensates for the disadvantages of single and mixed single models.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Their methods mainly combine the features of long short‐term memory (LSTM) and artificial neural network (ANN) and employ transfer learning to stop the modification of the backward propagation of LSTM layers and modify their output 19 . Al‐Qaness et al presented a metaheuristic optimisation algorithm for optimising the DNR model and used the model to forecast oil production 20 . Al‐Qaness et al applied a modified Aquila optimiser with the opposition‐based learning technique to forecast oil production 21 .…”
Section: Literature Reviewmentioning
confidence: 99%
“…These algorithms, and algorithms derived from their base have been applied in several fields with promising outcomes. Some noteworthy examples of metaheuristics applied to optimization problems include examples for crude oil price forecasting ( Jovanovic et al, 2022 ; Al-Qaness et al, 2022 ), Ethereum and Bitcoin prices predictions ( Stankovic et al, 2022b ; Milicevic et al, 2023 ; Petrovic et al, 2023 ; Gupta & Nalavade, 2022 ), industry 4.0 ( Jovanovic et al, 2023b ; Dobrojevic et al, 2023 ; Para, Del Ser & Nebro, 2022 ), medicine ( Zivkovic et al, 2022a ; Bezdan et al, 2022 ; Budimirovic et al, 2022 ; Stankovic et al, 2022a ), security ( Zivkovic et al, 2022b ; Savanović et al, 2023 ; Jovanovic et al, 2023c ; Zivkovic et al, 2022c ), cloud computing ( Thakur & Goraya, 2022 ; Mirmohseni, Tang & Javadpour, 2022 ; Bacanin et al, 2022d ; Zivkovic et al, 2021 ), and environmental sciences ( Jovanovic et al, 2023d ; Bacanin et al, 2022b ; Kiani et al, 2022 ).…”
Section: Background and Preliminariesmentioning
confidence: 99%
“…The support vector regression model hyperparameters were found through hybridizing cat mapping function, quantum computing concepts, and BAT algorithm, namely, CQBA 50 . In Reference 51, the authors employed the dendritic neural regression (DNR) model to forecast crude‐oil‐production, an ANNs model that has demonstrated promising performance in time‐series prediction. However, the DNR model encounters certain training and parameter configuration limitations.…”
Section: Related Workmentioning
confidence: 99%