Comparison of support vector regression and random forest algorithms for estimating the SOFC output voltage by considering hydrogen flow rates

İskenderoğlu, Feride Cansu; Baltacıoğlu, Mustafa Kaan; Demir, Mehmet Hakan; Baldinelli, Arianna; Barelli, Linda; Bidini, Gianni

doi:10.1016/j.ijhydene.2020.07.265

Cited by 60 publications

(13 citation statements)

References 52 publications

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“…Research done by F.C. ˙Iskendero gl and colleagues compared two efficient ML techniques, Random Forest (RF) and Support Vector Regression (SVR) [109]. These methods are used to forecast SOFC cell performance.…”

Section: Random Forestmentioning

confidence: 99%

A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis

et al. 2022

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In recent years, fuel cell (FC) technology has seen a promising increase in its proportion in stationary power production. Several pilot projects are in operation across the world, with the number of running hours steadily rising, either as stand-alone units or as part of integrated gas turbine–electric energy plants. FCs are a potential energy source with great efficiency and zero emissions. To ensure the best performance, they normally function within a confined temperature and humidity range; nevertheless, this makes the system difficult to regulate, resulting in defects and hastened deterioration. For diagnosis, there are two primary approaches: restricted input information, which gives an unobtrusive, rapid yet restricted examination, and advanced characterization, which provides a more accurate diagnosis but frequently necessitates invasive or delayed tests. Artificial Intelligence (AI) algorithms have shown considerable promise in providing accurate diagnoses with quick data collecting. This work focuses on software models that allow the user to evaluate many different possibilities in the shortest amount of time and is a vital method for proper and dynamic analysis of such entities. The artificial neural network, genetic algorithm, particle swarm optimization, random forest, support vector machine, and extreme learning machine are common AI approaches discussed in this review. This article examines the modern practice and provides recommendations for future machine learning methodologies in fuel cell diagnostic applications. In this study, these six AI tools are specifically explained with results for a better understanding of the fuel cell diagnosis. The conclusion suggests that these approaches are not only a popular and beneficial tool for simulating the nature of an FC system, but they are also appropriate for optimizing the operational parameters necessary for an ideal FC device. Finally, observations and ideas for future research, enhancements, and investigations are offered.

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Section: Random Forestmentioning

confidence: 99%

A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis

et al. 2022

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“…In addition, () x  is a non-linear transfer function applied to map the linear/non-linear original input dataset to the higher dimensional feature space. With the aim of enhancing the smoothness and simplicity of the SVR model, w is selected as small as possible (İskenderoğlu et al, 2020;Yu et al, 2006). Based on theory of structural risk minimization, w and b can be determined by the following formula:…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Novel Hybrid Models by Coupling Support Vector Regression (SVR) With Meta-heuristic Algorithms (WOA and GWO) for Urban Flood Risk Mapping

Rezaie

Panahi

Bateni

et al. 2022

Preprint

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Schools as social bases and children’s centers are among the most vulnerable areas to flooding. Flood risk mapping (FRM) is very important for flood preparedness and adopting preventive plans for reducing the school vulnerability to flooding. To achieve this, there is a need for the models that can be used in vast areas with high predictive accuracy. This study aims to develop the innovative hybrid models by coupling the support vector regression (SVR), statistical approaches, and two meta-heuristic algorithms, whale optimization algorithms (WOA) as well as grey wolf optimizer (GWO). According to the proposed methodology, a hybrid feature of SVR and frequency ratio (FR-SVR) is optimized by applying the GWO and WOA optimization algorithms to generate the maps related to flood susceptibility (FSMs). The method was utilized for the Ardabil Province located in southwestern Caspian Sea precincts of which faced devastating floods. The GIS database including 147 ground control locations of flooded zones and nine factors which influence flood were utilized to learn and ascertain the validity of the models. The statistical measures of RMSE, MAE, AUC, and ROC curve were then applied for the developed models in order to estimate prophetically. The results indicated that the meta-optimized FR-SVR-GWO as well as FR-SVR-WOA models exceeded the FR-SVR and FR models in training (RMSEFR-SVR-WOA = 0.2016, RMSEFR-SVR-GWO = 0.1885, AUCFR-SVR-WOA = 0.87, AUCFR-SVR-GWO = 0.88) and validation (RMSEFR-SVR-WOA = 0.2025, RMSEFR-SVR-GWO = 0.1986, AUCFR-SVR-WOA = 0.87, AUCFR-SVR-GWO = 0.87) phases. The FR-SVR-WOA and FR-SVR-GWO models were very competitive regarding AUC and RMSE values, but the FR-SVR-WOA model reproduced greater flood susceptibility rates and was considered for school flood risk mapping (SFRM).

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“…It addresses nonlinear problems conveniently through nonlinear mapping functions and efficiently handles small samples of datasets with excellent predictive outputs [ 15 , 16 ]. The algorithm has enjoyed wider applicability lately due to its robust mathematical computation and global convergence feature [ 17 , 18 , 19 , 20 , 21 , 22 ]. The hyperparameters associated with SVR include the epsilon, penalty factor, kernel function, and kernel parameter.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Optical Properties of a Polyvinyl Alcohol-Based Composite Using a Particle Swarm Optimized Support Vector Regression Algorithm

Owolabi

Rahman

2021

Polymers

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We developed particle swarm optimization-based support vector regression (PSVR) and ordinary linear regression (OLR) models for estimating the refractive index (n) and energy gap (E) of a polyvinyl alcohol composite. The n-PSVR model, which can estimate the refractive index of a polyvinyl alcohol composite using the energy gap as a descriptor, performed better than the n-OLR model in terms of root mean square error (RMSE) and mean absolute error (MAE) metrics. The E-PSVR model, which can predict the energy gap of a polyvinyl alcohol composite using its refractive index descriptor, outperformed the E-OLR model, which uses similar descriptor based on several performance measuring metrics. The n-PSVR and E-PSVR models were used to investigate the influences of sodium-based dysprosium oxide and benzoxazinone derivatives on the energy gaps of a polyvinyl alcohol polymer composite. The results agreed well with the measured values. The models had low mean absolute percentage errors after validation with external data. The precision demonstrated by these predictive models will enhance the tailoring of the optical properties of polyvinyl alcohol composites for the desired applications. Costs and experimental difficulties will be reduced.

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Comparison of support vector regression and random forest algorithms for estimating the SOFC output voltage by considering hydrogen flow rates

Cited by 60 publications

References 52 publications

A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis

A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis

Novel Hybrid Models by Coupling Support Vector Regression (SVR) With Meta-heuristic Algorithms (WOA and GWO) for Urban Flood Risk Mapping

Modeling the Optical Properties of a Polyvinyl Alcohol-Based Composite Using a Particle Swarm Optimized Support Vector Regression Algorithm

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