Accurate prediction of arsenic and lead contamination in karst groundwater using explainable ensemble machine learning

Data driven prediction of uranium in groundwater for environmental radioactivity surveillance

An integrated geochemical and explainable machine learning framework for mercury dynamics and risk assessment in the Musa Estuary.

Mercury (Hg) contamination in estuarine environments is of increasing concern because of its persistence, transformation into methylmercury (MeHg), and potential ecological and human health impacts. This study investigated the distribution, environmental controls, and health risks of Hg and MeHg in 41 surface sediment...

Refined simulation of groundwater level dynamics based on a machine learning stacking framework

Accurate and rapid prediction of groundwater levels (GWL) is essential for effective groundwater management. Machine learning models are efficient tools for GWL prediction, but individual models often suffer from limited generalization due to inherent randomness. This study proposed a stacking-based GWL prediction fr...

A machine learning approach to estimating nitrate-nitrogen concentrations in shallow groundwater across New Zealand.

Nitrate contamination of shallow groundwater is a concern for drinking water safety and ecosystem health globally, yet national-scale prediction techniques remain limited. Here, we describe the development and evaluation of an ensemble machine learning framework to estimate median nitrate (NO3-N) concentrations in shal...

Understanding Long-Term Groundwater Storage Variability Using GRACE Data and Explainable Machine Learning

A novel hybrid modeling framework integrating multi-source satellite and climate data with machine learning and explanatory artificial intelligence techniques for the long-term assessment and interpretation of GWS anomalies in the data-poor Iğdır Basin highlights the necessity of using system memory and explainable art...

Mapping Lead Contamination Hotspots in Topsoil’s of the Sokoto Basin Using Regression Kriging

Soil contamination by heavy metals poses a significant and persistent threat to ecosystem integrity, agricultural sustainability, and public health, necessitating accurate spatial assessment for effective mitigation. To address this need, this study implemented a regression kriging (RK) methodology to generate a high-r...