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Machine learning's application in solar-thermal desalination is limited bydata shortage and inconsistent analysis. This study develops an optimizeddataset collection and analysis process for the representative solar still. Byultra-hydrophilic treatment on the condensation cover, the dataset collectionprocess reduces the collection time by 83.3%. Over 1,000 datasets arecollected, which is nearly one order of magnitude larger than up-to-date works.Then, a new interdisciplinary process flow is proposed. Some meaningful resultsare obtained that were not addressed by previous studies. It is found thatRadom Forest might be a better choice for datasets larger than 1,000 due toboth high accuracy and fast speed. Besides, the dataset range affects thequantified importance (weighted value) of factors significantly, with up to a115% increment. Moreover, the results show that machine learning has a highaccuracy on the extrapolation prediction of productivity, where the minimummean relative prediction error is just around 4%. The results of this work notonly show the necessity of the dataset characteristics' effect but also providea standard process for studying solar-thermal desalination by machine learning,which would pave the way for interdisciplinary study.

The effect of dataset size and the process of big data mining for investigating solar-thermal desalination by using machine learning