A combined support vector regression with firefly algorithm for prediction of bottom hole pressure

Amar, Menad Nait; Zerhouni, Noureddine

doi:10.1007/s42452-019-1835-z

Cited by 25 publications

(8 citation statements)

References 31 publications

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“…Nonetheless, to increase the generalization of the model and avoid overfitting, slack variables ( ) 46 , 47 are used (see Fig. 3 ), which allow the model to have some miss-classified samples during training.…”

Section: Methodsmentioning

confidence: 99%

Application of power-law committee machine to combine five machine learning algorithms for enhanced oil recovery screening

Yousefzadeh,

Kazemi,

Al-Maamari

2024

Sci Rep

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One of the main challenges in screening of enhanced oil recovery (EOR) techniques is the class imbalance problem, where the number of different EOR techniques is not equal. This problem hinders the generalization of the data-driven methods used to predict suitable EOR techniques for candidate reservoirs. The main purpose of this paper is to propose a novel approach to overcome the above challenge by taking advantage of the Power-Law Committee Machine (PLCM) technique optimized by Particle Swam Optimization (PSO) to combine the output of five cutting-edge machine learning methods with different types of learning algorithms. The PLCM method has not been used in previous studies for EOR screening. The machine learning models include the Artificial Neural Network (ANN), CatBoost, Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The CatBoost is used for the first time in this work for screening of EOR methods. The role of the PSO is to find the optimal values for the coefficients and exponents of the power-law model. In this study, a bigger dataset than those in previous studies, including 2563 successful worldwide EOR experiences, was gathered. A bigger dataset improves the generalization of the data-driven methods and prevents overfitting. The hyperparameters of the individual machine-learning models were tuned using the fivefold cross-validation technique. The results showed that all the individual methods could predict the suitable EOR method for unseen cases with an average score of 0.868. Among the machine learning models, the KNN and SVM had the highest scores with a value of 0.894 and 0.892, respectively. Nonetheless, after combining the output of the models using the PLCM method, the score of the predictions improved to 0.963, which was a substantial increase. Finally, a feature importance analysis was conducted to find out the most influential parameters on the output. The novelty of this work is having shown the ability of the PLCM technique to construct an accurate model to overcome the class-imbalance issue in EOR screening by utilizing different types of data-driven models. According to feature importance analysis, oil gravity and formation porosity were recognized as the most influential parameters on EOR screening.

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Section: Methodsmentioning

confidence: 99%

Application of power-law committee machine to combine five machine learning algorithms for enhanced oil recovery screening

Yousefzadeh,

Kazemi,

Al-Maamari

2024

Sci Rep

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“…It can be seen from Figure 1 that most of the random numbers N i (0, 1) of Gauss distribution are in the interval of [23,3], and the random numbers C i (0, 1) of Cauchy distribution are concentrated in the interval of [25,5]. Therefore, when using the random number of Gauss distribution, individuals have a greater probability to search in the range near the expected value to find more accurate results.…”

Section: Double-population Adaptive Mutation Strategymentioning

confidence: 99%

“…Support vector regression (SVR) was proposed by Vapnik, 18 which is used to solve the fitting problem of nonlinear data in engineering, such as the prediction of vehicle air conditioning performance, 19 battery remaining useful life prediction 20 and so on. To improve the prediction accuracy of SVR, researchers propose to use various optimization algorithms, such as artificial fish swarm algorithm (AFSA), 21 GA, 22 firefly algorithm (FA), 23 to search for the optimal combination of parameters.…”

Section: Introductionmentioning

confidence: 99%

Control strategy research of electric vehicle thermal management system based on MGA-SVR algorithm

Wei

Mei

et al. 2022

Measurement and Control

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The thermal management system is one of the important assemblies that ensure the secure operation of electric vehicles (EVs). Using intelligent algorithms to optimize the control strategy of the thermal management system can reduce energy consumption under the premise of effective heat dissipation of EVs. This paper attempts to construct the control strategy of EV thermal management system by coupling the modified genetic algorithm (MGA) and support vector regression (SVR). Firstly, the double-population adaptive mutation method and a novel optimization process are adopted to obtain MGA. Afterward, the performance of MGA is verified by four benchmark functions compared with three typical algorithms, which are genetic algorithm (GA), double-population genetic algorithm (DPGA), and quantum genetic algorithm (QGA). The results demonstrate that the accuracy and stability of MGA are obviously better than the other three algorithms. Secondly, MGA is applied to modify parameters of SVR kernel function, and the accuracy of MGA-SVR algorithm is verified by the Auto-MPG and Computer Hardware data sets. The mean square deviations of the SVR algorithm test set are 0.0186 and 0.0806, respectively, and the mean square deviations of the MGA-SVR algorithm test set are 0.0099 and 0.0054, respectively, which fully shows that MGA-SVR have more accurate forecasting capabilities. Finally, the thermal management system model of EV is built by the one-dimensional simulation software KULI. Under the Chinese working condition, fan speed which meets the cooling requirements of the motor and controller is obtained from the KULI model, and the database is constructed. Then, MGA-SVR is trained by database and employed to predict fan speed under the Chinese working condition and obtain control strategy of the thermal management system. Compared with traditional control strategy, the thermal management system based on MGA-SVR control strategy can not only meet the radiating requirements, but also effectively reduce the power consumption of fans.

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“…RVM and SVM both require an optimizer for tuning their parameters, which determines the accuracy and generalization capability of the model. Many studies have introduced optimization techniques for both RVM and SVM, such as genetic algorithms 18 , grey wolf optimization 19 , 20 , firefly algorithm 21 , coyote optimization 22 , artificial bee colony 23 , and Bayesian optimization 24 .…”

Section: Introductionmentioning

confidence: 99%

Pattern and predictors of death from aluminum and zinc phosphide poisoning using multi-kernel optimized relevance vector machine

Abdelghafar

Farrag

Zanaty

et al. 2023

Sci Rep

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The use of metal phosphides, particularly aluminum phosphide, poses a significant threat to human safety and results in high mortality rates. This study aimed to determine mortality patterns and predictive factors for acute zinc and aluminum phosphide poisoning cases that were admitted to Menoufia University Poison and Dependence Control Center from 2017 to 2021. Statistical analysis revealed that poisoning was more common among females (59.7%), aged between 10 and 20 years, and from rural regions. Most cases were students, and most poisonings were the result of suicidal intentions (78.6%). A new hybrid model named Bayesian Optimization-Relevance Vector Machine (BO-RVM) was proposed to forecast fatal poisoning. The model achieved an overall accuracy of 97%, with high positive predictive value (PPV) and negative predictive value (NPV) values of 100% and 96%, respectively. The sensitivity was 89.3%, while the specificity was 100%. The F1 score was 94.3%, indicating a good balance between precision and recall. These results suggest that the model performs well in identifying both positive and negative cases. Additionally, the BO-RVM model has a fast and accurate processing time of 379.9595 s, making it a promising tool for various applications. The study underscores the need for public health policies to restrict the availability and use of phosphides in Egypt and adopt effective treatment methods for phosphide-poisoned patients. Clinical suspicion, positive silver nitrate test for phosphine, and analysis of cholinesterase levels are useful in diagnosing metal phosphide poisoning, which can cause various symptoms.

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A combined support vector regression with firefly algorithm for prediction of bottom hole pressure

Cited by 25 publications

References 31 publications

Application of power-law committee machine to combine five machine learning algorithms for enhanced oil recovery screening

Application of power-law committee machine to combine five machine learning algorithms for enhanced oil recovery screening

Control strategy research of electric vehicle thermal management system based on MGA-SVR algorithm

Pattern and predictors of death from aluminum and zinc phosphide poisoning using multi-kernel optimized relevance vector machine

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