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 …
Systematic review of machine learning applications in mining: Exploration, exploitation, and reclamation
D Jung, Y Choi - Minerals, 2021 - mdpi.com
Recent developments in smart mining technology have enabled the production, collection,
and sharing of a large amount of data in real time. Therefore, research employing machine …
and sharing of a large amount of data in real time. Therefore, research employing machine …
Heavy metal contamination prediction using ensemble model: Case study of Bay sedimentation, Australia
Lead (Pb) is a primary toxic heavy metal (HM) which present throughout the entire
ecosystem. Some commonly observed challenges in HM (Pb) prediction using artificial …
ecosystem. Some commonly observed challenges in HM (Pb) prediction using artificial …
Monitoring soil lead and zinc contents via combination of spectroscopy with extreme learning machine and other data mining methods
In order to limit pollution risk and develop proper remediation strategies, soil quality has to
be controlled by rapid and sustainable monitoring measures. Visible and near-infrared …
be controlled by rapid and sustainable monitoring measures. Visible and near-infrared …
Prediction of copper ions adsorption by attapulgite adsorbent using tuned-artificial intelligence model
Copper (Cu) ion in wastewater is considered as one of the crucial hazardous elements to be
quantified. This research is established to predict copper ions adsorption (Ad) by Attapulgite …
quantified. This research is established to predict copper ions adsorption (Ad) by Attapulgite …
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 …
Effectiveness of groundwater heavy metal pollution indices studies by deep-learning
Globally, groundwater heavy metal (HM) pollution is a serious concern, threatening drinking
water safety as well as human and animal health. Therefore, evaluation of groundwater HM …
water safety as well as human and animal health. Therefore, evaluation of groundwater HM …
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 …
A comprehensive support vector machine-based classification model for soil quality assessment
Y Liu, H Wang, H Zhang, K Liber - Soil and Tillage Research, 2016 - Elsevier
Soil quality is defined here as the capacity of soil to have biological function, to sustain plant
and animal production, to maintain or enhance water and air quality and to support human …
and animal production, to maintain or enhance water and air quality and to support human …
Rapid diagnosis of heavy metal pollution in lake sediments based on environmental magnetism and machine learning
X Li, Y Yang, J Yang, Y Fan, X Qian, H Li - Journal of Hazardous Materials, 2021 - Elsevier
Environmental magnetism in combination with machine learning can be used to monitor
heavy metal pollution in sediments. Magnetic parameters and heavy metal concentrations of …
heavy metal pollution in sediments. Magnetic parameters and heavy metal concentrations of …