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

Abstract

Accurate forecasting of freshwater productivity from conventional single-slope solar stills is crucial for enhancing operational efficiency and minimizing capital costs. A persistent challenge in this domain is the scarcity of experimental data, which limits the training of reliable predictive models. This study proposes a data-efficient forecasting framework that integrates a one-dimensional convolutional neural network (CNN-1D) with time-series data augmentation. Gaussian noise sampled from $N$(0, 0.01²) was applied exclusively to the training set, generating six augmented samples per instance. Both the augmentation factor (six) and the look-back window (seven days) were selected through systematic optimization, ensuring preservation of temporal dependencies. The CNN-1D architecture comprised three convolutional layers with 128 filters, ReLU activations, a flattening stage, and a dense regression output layer. Hyperparameters—including learning rate, batch size, kernel size, and regularization strength—were fine-tuned using Tree-structured Parzen Estimator (TPE) optimization with a maximum of 50 trials, where the best-performing configuration achieved the lowest loss. Model training employed a feed-forward backpropagation algorithm with 365 daily observations to predict freshwater yield (P_std, L/day). Benchmarking against an optimized support vector regression (SVR) model with a radial basis function kernel revealed that the augmented CNN-1D achieved superior performance (RMSE = 0.04, MAE = 0.03, OIMP = 0.97), consistently outperforming both the baseline CNN-1D and the optimized SVR. Residual analyses confirmed its robustness, minimal bias, and strong generalization across unseen data. These findings demonstrate that combining augmentation with hierarchical feature extraction enables a scalable and computationally efficient predictive tool for solar still performance, offering significant potential for sustainable freshwater management in arid and data-constrained regions.