Description

This repository contains codes related to the publication “Exploring machine learning models to predict atmospheric water harvesting with an ion deposition membrane”. Datasets and trained models are published on our Zenodo repository.

In particular:

  • Folder HP tuning contains all .ipynb files for finding the optimal hyper-parameter combinations through grid-search optimization. Specifically, each file will run a grid-search optimization over 50 different and mutually exclusive splits of training and testing set. The optimal combination is chosen according to a majority vote strategy.
  • Model_comparison.ipynb trains the ML models and compares their accuracy and stability.
  • Simulation.ipynb makes predictions over real world data.
Overview of the protocol for training the ML models.

Citation

Barletta, G.; Moitra, S.; Derrible, S. ; Mathew, A. ; Nair, A. M. ; Megaridis, C. M. Exploring machine learning models to predict atmospheric water harvesting with an ion deposition membrane. J. Water Process Eng., 2025, https://doi.org/10.1016/j.jwpe.2025.107476

@article{BARLETTA2025107476,
TITLE = {Exploring machine learning models to predict atmospheric water harvesting with an ion deposition membrane},
JOURNAL = {Journal of Water Process Engineering},
VOLUME = {72},
PAGES = {107476},
YEAR = {2025},
ISSN = {2214-7144},
DOI = {https://doi.org/10.1016/j.jwpe.2025.107476},
URL = {https://www.sciencedirect.com/science/article/pii/S2214714425005483},
AUTHOR = {Barletta, Giulio and Moitra, Shashwata and Derrible, Sybil and Mathew, Alex and Nair, Anoop Muraleedharan and Megaridis, Constantine M.},
KEYWORDS = {Machine learning, Atmospheric water harvesting, Membrane},
}