Advancing predictive modeling in conventional solar stills: a deep learning approach leveraging data augmentation and convolutional neural networks

[Display omitted] •Gaussian noise-based augmentation generated six synthetic samples per input, boosting data efficiency.•An optimized CNN-1D (5-128-128-128-1) was developed through systematic hyperparameter tuning.•Augmented CNN-1D achieved R2 = 0.97, RMSE = 0.04, MAE = 0.03 , outperforming baselin...

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Vydané v:	Energy conversion and management Ročník 346; s. 120565
Hlavní autori:	Migaybil, Hashim H., Gopaluni, Bhushan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 15.12.2025
Predmet:	Convolutional neural networks (CNNs) Data augmentation Distillate water Look-back window Solar still Support vector regression (SVR) Data augmentation Solar still Support vector regression (SVR) Convolutional neural networks (CNNs) Look-back window Distillate water
ISSN:	0196-8904
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