A Hybrid Machine Learning Approach for Daily Prediction of Solar Radiation

Torabi, Mehrnoosh; Mosavi, Amir; Öztürk, Pınar; Várkonyi-Kóczy, Annamária R.; Vajda, István

doi:10.1007/978-3-319-99834-3_35

Cited by 48 publications

(32 citation statements)

References 26 publications

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“…Although the application of machine learning for prediction of pollutants and mercury emissions is well established within the scientific community, the potential of novel machine learning models (e.g., ensembles and hybrids) has still not been explored for mercury prediction. In particular, a wide range of novel hybrid machine learning methods has recently been developed to deliver higher accuracy and performance [47,76,77]. For instance, the hybrid model of the ANFIS-PSO-which is an integration of an adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO)-has shown promising results [78].…”

Section: Literature Reviewmentioning

confidence: 99%

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

et al. 2019

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Accurate prediction of mercury content emitted from fossil-fueled power stations is of the utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model included coal characteristics and the operational parameters of the boilers. The dataset was collected from 82 sample points in power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of the ANFIS-PSO, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between the acquired data and predicted values were between −0.25% and 0.1%, which confirm the accuracy of the model to deal non-linearity and represent the dependency of flue gas mercury content into the specifications of coal and the boiler type.

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Section: Literature Reviewmentioning

confidence: 99%

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

et al. 2019

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“…Dragomir Although the application of machine learning for prediction of pollutants and mercury emissions is well established within the scientific communities, the potential of the novel machine learning models (e.g., ensembles and hybrids) is still not explored for mercury prediction. In particular, a wide range of novel hybrid machine learning methods has been recently developed to deliver higher accuracy and performance [47,76,77]. For instance, the hybrid model of ANFIS-PSO which is an integration of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) has shown to deliver promising results [78].…”

Section: Previous Investigationsmentioning

confidence: 99%

Developing ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Shamshirband¹,

Hadipoor²,

Baghban³

et al. 2019

Preprint

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Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations' boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model include coal characteristics and the operational parameters of the boilers. The dataset has been collected from 82 power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of ANFIS-PSO model, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively.Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, which confirm the accuracy of the model to deal nonlinearity and representing the dependency of flue gas mercury content into the specifications of coal and the boiler type.

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“…The GeneXproTools 5.0 was used to develop the GEP-based prediction equation in MATLAB [42]. The performance of developed GEP models was evaluated using coefficient of determination (R 2 ), root mean squared error (RMSE), and mean average error (MAE) (21)(22)(23), applying the following equations:…”

Section: Model Structure and Performancementioning

confidence: 99%

“…Soft computing techniques such as artificial neural networks (ANN) are widely accepted and popular along the conventional statistical methods (e.g., regression) [11][12][13][14][15][16][17][18][19][20][21] . These techniques were successfully applied to different geotechnical problems such as Cc prediction [7,[22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Prediction Model for Fine-Grained Soils Compression Index Using Gene-Expression Programming

Mohammadzadeh¹,

Kazemi²,

Mosavi³

2019

Preprint

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Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time.Coefficient of consolidation (Cc) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, Cc was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate Cc such as oedometer test. This study presents a novel prediction model for Cc of finegrained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of developed GEP-based model was evaluated through coefficient of determination (R 2 ), root mean squared error (RMSE), and mean average error (MAE). High R 2 and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R 2 , RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining Cc of fine-grained soils.

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A Hybrid Machine Learning Approach for Daily Prediction of Solar Radiation

Cited by 48 publications

References 26 publications

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Developing ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Evolutionary Prediction Model for Fine-Grained Soils Compression Index Using Gene-Expression Programming

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