  Presented is a new generation prediction model of a tubular solar still (TSS)
productivity utilizing two machine learning (ML) techniques, namely:Random
forest (RF) and Artificial neural network (ANN). Prediction models were
conducted based on experimental data recorded under Egyptian climate.
Meteorological and operational thermal parameters were utilized as input
layers. Moreover, Bayesian optimization algorithm (BOA) was used to obtain the
optimal performance of RF and ANN models. In addition, these models results
were compared to those of a multilinear regression (MLR) model. As resulted,
experimentally, the average value accumulated productivity was 4.3 L/(m2day).
For models results, RF was less sensitive to hyper parameters than ANN as ANN
performance could be significantly improved by BOA more than RF. In addition,
RF achieved better prediction performance of TSS on the current dataset. The
determination coefficients (R2) of RF and ANN were 0.9964 and 0.9977,
respectively, which were much higher than MLR models, 0.9431. Based on the
robustness performance and high accuracy, RF is recommended as a stable method
for predicting the productivity of TSS.
