Development of artificial intelligence for modeling wastewater heavy metal removal: State of the art, application assessment and possible future research
The presence of various forms of heavy metals (HMs)(eg, Cu, Cd, Pb, Zn, Cr, Ni, As, Co, Hg,
Fe, Mn, Sb, and Ce) in water bodies and sediment has been increasing due to industrial and …
Fe, Mn, Sb, and Ce) in water bodies and sediment has been increasing due to industrial and …
[HTML][HTML] Artificial intelligence in heavy metals detection: Methodological and ethical challenges
N Yadav, BM Maurya, D Chettri, C Pulwani… - Hygiene and …, 2023 - Elsevier
Heavy metals (HMs) are metallic substances. They enter biotic and abiotic systems through
natural and human activities. These HMs have an impact on the atmosphere, soil, and …
natural and human activities. These HMs have an impact on the atmosphere, soil, and …
Application of artificial neural networks to predict the heavy metal contamination in the Bartin River
In this study, copper (Cu), iron (Fe), zinc (Zn), manganese (Mn), nickel (Ni), and lead (Pb)
analyses were performed, and the results were modelled by artificial neural networks (ANN) …
analyses were performed, and the results were modelled by artificial neural networks (ANN) …
Manganese (Mn) removal prediction using extreme gradient model
Abstract Exploring the Manganese (Mn) removal prediction with several independent
variables is tremendously critical and indispensable to understand the pattern of removal …
variables is tremendously critical and indispensable to understand the pattern of removal …
Intelligent soft computational models integrated for the prediction of potentially toxic elements and groundwater quality indicators: a case study
JC Agbasi, JC Egbueri - Journal of sedimentary environments, 2023 - Springer
Reports have shown that potentially toxic elements (PTEs) in air, water, and soil systems
expose humans to carcinogenic and non-carcinogenic health risks. In southeastern Nigeria …
expose humans to carcinogenic and non-carcinogenic health risks. In southeastern Nigeria …
A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction
Predictive modeling of hydrological time series is essential for groundwater resource
development and management. Here, we examined the comparative merits and demerits of …
development and management. Here, we examined the comparative merits and demerits of …
Estimation of heavy metals in agricultural soils using vis-NIR spectroscopy with fractional-order derivative and generalized regression neural network
X Xu, S Chen, L Ren, C Han, D Lv, Y Zhang, F Ai - Remote Sensing, 2021 - mdpi.com
With the development of industrialization and urbanization, heavy metal contamination in
agricultural soils tends to accumulate rapidly and harm human health. Visible and near …
agricultural soils tends to accumulate rapidly and harm human health. Visible and near …
Simulating heavy metal concentrations in an aquatic environment using artificial intelligence models and physicochemical indexes
H Lu, H Li, T Liu, Y Fan, Y Yuan, M Xie… - Science of the Total …, 2019 - Elsevier
Although heavy metal monitoring campaigns are established worldwide, it is still difficult to
model heavy metals in aquatic environments with limited monitoring data. In this study …
model heavy metals in aquatic environments with limited monitoring data. In this study …
Machine learning-based prediction of toxic metals concentration in an acid mine drainage environment, northern Tunisia
Abstract In northern Tunisia, Sidi Driss sulfide ore valorization had produced a large waste
amount. The long tailings exposure period and in situ minerals interactions produced an …
amount. The long tailings exposure period and in situ minerals interactions produced an …
An adaptive neuro-fuzzy inference system (ANFIS) to predict of cadmium (Cd) concentrations in the Filyos River, Turkey
Water quality is one of the main characteristics of a river system and prediction of water
quality is the key factor in water resource management. Different physical, biological and …
quality is the key factor in water resource management. Different physical, biological and …