There is growing tension between high-performance machine-learning (ML) models and
explainability within the scientific community. In arsenic modelling, understanding why ML …

Access to clean and safe drinking water is key to the improvement of social lives in most
developing countries. Due to its hazardous nature and detrimental effects on human health …

Arsenic contamination is increasingly a serious global health concern, especially in
developing countries like Ghana where the intensification of mining activities has resulted in …

The current study proposes an effective machine learning model based on a stacked
generalisation technique for predicting arsenic content in water sources (groundwater …

Groundwater contamination with arsenic (As) is one of the major issues in the world,
especially for Southeast Asian (SEA) countries where groundwater is the major drinking …

Water quality assessment and prediction play crucial roles in ensuring the sustainability and
safety of freshwater resources. This study aims to enhance water quality assessment and …

The global significance of fluoride and nitrate contamination in coastal areas cannot be
overstated, as these contaminants pose critical environmental and public health challenges …

Arsenic (As) contamination in groundwater represents a major global health threat,
potentially impacting billions of individuals. Elevated As concentrations are found in river …

In the 40 years since the relation between arsenic (As) toxicity and groundwater extraction
was first documented from the Holocene alluvial basin of West Bengal, India, 1 we have …

Long-term exposure to geogenic arsenic (As)-contaminated groundwater poses a severe
threat to public health problems. Generally, elevated As concentrations have been observed …