Open AccessMachine learning in vadose zone hydrology: A flashback
Author(s) -
Ghanbarian Behzad,
Pachepsky Yakov
Publication year - 2022
Publication title -
vadose zone journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.036
H-Index - 81
ISSN - 1539-1663
DOI - 10.1002/vzj2.20212
Subject(s) - vadose zone , hydrology (agriculture) , computer science , environmental science , path (computing) , artificial intelligence , geology , soil science , geotechnical engineering , soil water , programming language
Artificial intelligence (AI) and machine learning (ML) have been recently applied extensively in various disciplines of vadose zone hydrology. However, not much attention has been paid to their database‐dependent accuracy and uncertainty, reproducibility, and delivery, which undermines their applications to real‐world problems. We discuss lessons from the past and emphasize the need for and lack of fundamental protocols (i.e., detailed clarification on data processing, ML models accessibility, and a clear path for reproducing results).