• Energy Research
• 2025-2025close
• 1. No poverty close
• 6. Clean water close
• CN close
• PK close

Machine learning's application in solar-thermal desalination is limited by data shortage and inconsistent analysis. This study develops an optimized dataset collection and analysis process for the representative solar still. By ultra-hydrophilic treatment on the condensation cover, the dataset collection process reduces the collection time by 83.3%. Over 1,000 datasets are collected, which is nearly one order of magnitude larger than up-to-date works. Then, a new interdisciplinary process flow is proposed. Some meaningful results are obtained that were not addressed by previous studies. It is found that Radom Forest might be a better choice for datasets larger than 1,000 due to both high accuracy and fast speed. Besides, the dataset range affects the quantified importance (weighted value) of factors significantly, with up to a 115% increment. Moreover, the results show that machine learning has a high accuracy on the extrapolation prediction of productivity, where the minimum mean relative prediction error is just around 4%. The results of this work not only show the necessity of the dataset characteristics' effect but also provide a standard process for studying solar-thermal desalination by machine learning, which would pave the way for interdisciplinary study.

Governments prioritize global food insecurity. Food insecurity affects a billion people, with Asia and the Pacific Islands having the highest rates and Sub-Saharan Africa the lowest. Pakistan is one of the worst-hit countries due to a surge in chronically food insecure people. Pakistan's food crisis persists. Due to improved worldwide methods and statistics on population, food balance sheets, and consumption patterns, Pakistan's PoU for 2017-19 is 12.3%, up from 12.0% the year before. This is helping measure progress on SDG indicator 2.1.1. 26 million individuals cannot fulfill their basic calorie needs, and the number is rising