A critical appraisal of water table depth estimation: Challenges and opportunities within machine learning

arXiv:2405.04579v1 Announce Type: new
Abstract: Fine-resolution spatial patterns of water table depth (WTD) can inform the dynamics of groundwater-dependent systems, including ecological, hydrological, and anthropogenic systems. Generally, a large-scale (e.g., continental or global) spatial map of static WTD can be simulated using either physically-based (PB) or machine learning-based (ML) models. We construct three fine-resolution (500 m) ML simulations of WTD, using the XGBoost algorithm and more than 20 million real and proxy observations of WTD, across the United States and …

abstract arxiv challenges continental cs.lg dynamics global machine machine learning map opportunities patterns resolution scale spatial systems table type water