Optimized data collection and analysis process for studying
  solar-thermal desalination by machine learning
Guilong Peng Senshan Sun Yangjun Qin Zhenwei Xu Juxin Du Swellam W. sharshir A. W. Kandel A. E. Kabeel Nuo Yang
An effective interdisciplinary study between machine learning and
solar-thermal desalination requires a sufficiently large and well-analyzed
experimental datasets. This study develops a modified dataset collection and
analysis process for studying solar-thermal desalination by machine learning.
Based on the optimized water condensation and collection process, the proposed
experimental method collects over one thousand datasets, which is ten times
more than the average number of datasets in previous works, by accelerating
data collection and reducing the time by 83.3%. On the other hand, the effects
of dataset features are investigated by using three different algorithms,
including artificial neural networks, multiple linear regressions, and random
forests. The investigation focuses on the effects of dataset size and range on
prediction accuracy, factor importance ranking, and the model's generalization
ability. The results demonstrate that a larger dataset can significantly
improve prediction accuracy when using artificial neural networks and random
forests. Additionally, the study highlights the significant impact of dataset
size and range on ranking the importance of influence factors. Furthermore, the
study reveals that the extrapolation data range significantly affects the
extrapolation accuracy of artificial neural networks. Based on the results,
massive dataset collection and analysis of dataset feature effects are
important steps in an effective and consistent machine learning process flow
for solar-thermal desalination, which can promote machine learning as a more
general tool in the field of solar-thermal desalination.