Prediction of tubular solar still performance by machine learning integrated with Bayesian optimization algorithm

•Two machine learning models were developed to predict the productivity of tubular solar still.•Bayesian optimization algorism was considered for both models.•Optimized models more accurately predicted production with better evaluation indicators.•Random forest was less sensitive to hyper parameters...

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Vydáno v:	Applied thermal engineering Ročník 184; s. 116233
Hlavní autoři:	Wang, Yunpeng, Kandeal, A.W., Swidan, Ahmed, Sharshir, Swellam W., Abdelaziz, Gamal B., Halim, M.A., Kabeel, A.E., Yang, Nuo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Elsevier Ltd 05.02.2021 Elsevier BV
Témata:	Algorithms Artificial intelligence Artificial neural network Artificial neural networks Bayesian analysis Bayesian optimization Machine learning Mathematical models Neural networks Optimization Parameter sensitivity Prediction models Random forest Regression model Studies Tubular solar still Random forest Artificial neural network Bayesian optimization Regression model Tubular solar still Machine learning
ISSN:	1359-4311, 1873-5606
On-line přístup:	Získat plný text
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