Machine learning in vadose zone hydrology: A flashback

Abstract

AbstractArtificial 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).