An interpretable machine learning approach for alkalinity reconstruction in the Mediterranean Sea

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@Article{tonelli:2026:acags,

• author = "Teresa Tonelli and Gloria Pietropolli and Luigi Rovito and Luca Manzoni and Gianpiero Cossarini",
• title = "An interpretable machine learning approach for alkalinity reconstruction in the Mediterranean Sea",
• journal = "Applied Computing and Geosciences",
• year = "2026",
• volume = "30",
• pages = "100345",
• keywords = "genetic algorithms, genetic programming, Alkalinity, PH, Mediterranean Sea, Machine learning, Symbolic regression, Artificial intelligence, XAI",
• ISSN = "2590-1974",
• URL = "https://arts.units.it/handle/11368/3133758",
• URL = "https://hdl.handle.net/11368/3133758",
• URL = "https://www.sciencedirect.com/science/article/pii/S2590197426000297",
• DOI = "10.1016/j.acags.2026.100345",
• size = "12 pages",
• abstract = ".. we use the derived alkalinity equations to produce gap-free 2D surface alkalinity maps using satellite data...",
• notes = "also known as \cite{TONELLI2026100345}",

}

Genetic Programming entries for Teresa Tonelli Gloria Pietropolli Luigi Rovito Luca Manzoni Gianpiero Cossarini

Citations