Robust prediction of water arsenic levels downstream of gold mines affected by acid mine drainage using hybrid ensemble machine learning and soft computing.

Water pollution from hazardous materials, particularly arsenic, downstream of gold mines poses severe environmental and health risks. This study employs a systematic approach to predict water arsenic (WA) levels downstream of gold mines affected by acid mine drainage. WA data from the affected region were collected and preprocessed to standardize the dataset and mitigate overfitting risks.

Integrated optimization of marine oil spill response and liquid oily waste management using mathematical programming and evolutionary metaheuristic techniques.

Marine oil spill response is a time sensitive and complex task, in which the management of oily wastewater generated from response operations could be a bottleneck limiting the response capacity and efficiency. This study developed a multi-agent decision support system to effectively coordinate mechanical containment and recovery (MCR) of spilt oil and oily wastewater management (OWM) operations. The system aims to minimize the overall response time, cost, and the volume of weathered oil by applying evolutionary optimization, oil weathering process, and response operational agents.

Chemical composition of arsenic-based acid mine drainage in the downstream of a gold mine: Fuzzy regression and clustering analysis.

This study employs fuzzy regression and fuzzy multivariate clustering techniques to analyze arsenic-polluted water samples originating from acid rock drainage in waste rock dumps. The research focuses on understanding the complex relationships between variables associated with arsenic contamination, such as water arsenic concentration, pH levels, and soil characteristics. To this end, fuzzy regression models were developed to estimate the relationships between water arsenic concentration and independent variables, thus, incorporating the inherent uncertainties into the analysis.

Staged energy and water quality optimization for large water distribution systems.

Simultaneous optimization of energy and water quality in real-time large-sized water distribution systems is a daunting task for water suppliers. The complexity of energy optimization increases with a large number of pipes, scheduling of several pumps, and adjustments of tanks' water levels. Most of the simultaneous energy and water quality optimization approaches evaluate small (or hypothetical) networks or compromise water quality.

Heavy metals risk assessment in drinking water: An integrated probabilistic-fuzzy approach.

Heavy metal(loids) in drinking water have long been a critical water quality concern. Chronic exposure to toxic heavy metals and metalloids (TMMs) through water ingestion can result in significant health risks to the public, while elevated concentrations of less toxic heavy metals (LTMs) can compromise the aesthetic value of water. An integrated probabilistic-fuzzy approach was developed to help water utilities assess water quality regarding heavy metal(loids) (WQHM).