Predicting bioavailability of potentially toxic elements (PTEs) in sediment using various machine learning (ML) models: A case study in Mahabad Dam and River-Iran
Considering the significant impact of potentially toxic elements (PTEs) on the ecosystem and
human health, this paper, investigated the contamination level of four PTEs (Zn, Cu, Mo and …
human health, this paper, investigated the contamination level of four PTEs (Zn, Cu, Mo and …
Tracking the impact of heavy metals on human health and ecological environments in complex coastal aquifers using improved machine learning optimization
The rising heavy metal (HM) pollution in coastal aquifers in rapidly urbanizing areas such as
Dammam leads to significant risks to public health and environmental sustainability …
Dammam leads to significant risks to public health and environmental sustainability …
[HTML][HTML] Long-term natural streamflow forecasting under drought scenarios using data-intelligence modeling
LD Balthazar, F Miranda, VBR Cândido, P Capriles… - Water Cycle, 2024 - Elsevier
Long-term river streamflow prediction and modeling are essential for water resource
management and decision-making related to water resources. This research paper …
management and decision-making related to water resources. This research paper …
Machine learning models with innovative outlier detection techniques for predicting heavy metal contamination in soils
Abstract Machine learning (ML) models for accurately predicting heavy metals with
inconsistent outputs have improved owing to dataset outliers, which influence model …
inconsistent outputs have improved owing to dataset outliers, which influence model …
Hybrid weights structure model based on Lagrangian principle to handle big data challenges for identification of oil well production: A case study on the North Basra …
RZ Homod, AS Albahri, BS Munahi… - … Applications of Artificial …, 2024 - Elsevier
The identification of the oilfield production flow rate, which is a function of the wellhead
pressure, where both are characterized as a complex, nonlinear stochastic dynamical …
pressure, where both are characterized as a complex, nonlinear stochastic dynamical …
Assessing petrochemical effluent effect on heavy metal pollution in Musa Estuary: A numerical modeling approach
The objective of this study is to assess the effect of petrochemical effluent on heavy metal
pollutant in the Musa Estuary ecosystem in the North-western region of the Persian Gulf …
pollutant in the Musa Estuary ecosystem in the North-western region of the Persian Gulf …
Data-intelligence approaches for comprehensive assessment of discharge coefficient prediction in cylindrical weirs: Insights from extensive experimental data sets
The present study collected a wide range of experimental data samples, including 855
records from various types of cylindrical weirs under diverse hydraulic conditions, setting the …
records from various types of cylindrical weirs under diverse hydraulic conditions, setting the …
[HTML][HTML] Salmon Salar Optimization: A Novel Natural Inspired Metaheuristic Method for Deep-Sea Probe Design for Unconventional Subsea Oil Wells
As global energy demands continue to rise, the development of unconventional oil
resources has become a critical priority. However, the complexity and high dimensionality of …
resources has become a critical priority. However, the complexity and high dimensionality of …
Screening and Optimization of Soil Remediation Strategies Assisted by Machine Learning
B Zhang, X Wang, C Liu - Processes, 2024 - mdpi.com
A numerical approach assisted by machine learning was developed for screening and
optimizing soil remediation strategies. The approach includes a reactive transport model for …
optimizing soil remediation strategies. The approach includes a reactive transport model for …