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 oilfield, Iraq DOI
Raad Z. Homod, A. S. Albahri, Basil Sh. Munahi

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 138, P. 109465 - 109465

Published: Oct. 18, 2024

Language: Английский

Evidential-bio-inspired algorithms for modeling groundwater total hardness: A pioneering implementation of evidential neural network for feature selection in water resources management DOI Creative Commons
A. G. Usman, Abdulhayat M. Jibrin, Sagiru Mati

et al.

Environmental Chemistry and Ecotoxicology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

3
Data-intelligence approaches for comprehensive assessment of discharge coefficient prediction in cylindrical weirs: Insights from extensive experimental data sets DOI
Kiyoumars Roushangar, Saman Shahnazi,

Amir Mehrizad

et al.

Measurement, Journal Year: 2024, Volume and Issue: 233, P. 114673 - 114673

Published: April 8, 2024

Language: Английский

Citations

4
Tracking the impact of heavy metals on human health and ecological environments in complex coastal aquifers using improved machine learning optimization DOI
Abdulhayat M. Jibrin, Sani I. Abba, Jamilu Usman

et al.

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(40), P. 53219 - 53236

Published: Aug. 24, 2024

Language: Английский

Citations

4
Spatial distribution, contamination levels, and risk assessment of heavy metals along the Eastern India coastline DOI
Mrunmayee Manjari Sahoo, Janaki Ballav Swain

Marine Pollution Bulletin, Journal Year: 2025, Volume and Issue: 214, P. 117779 - 117779

Published: March 9, 2025

Language: Английский

Citations

0
Evaluation of machine learning models for accurate prediction of heavy metals in coal mining region soils in Bangladesh DOI
Ram Proshad,

Krishno Chandra,

Maksudul Islam

et al.

Environmental Geochemistry and Health, Journal Year: 2025, Volume and Issue: 47(5)

Published: April 23, 2025

Language: Английский

Citations

0
Base modified mesoporous silica adsorbent for heavy metal adsorption: Optimization of adsorption efficiency with machine learning algorithms DOI Creative Commons

Shital Tank,

Madhu Pandey, Jagat Jyoti Rath

et al.

Hybrid Advances, Journal Year: 2025, Volume and Issue: unknown, P. 100489 - 100489

Published: April 1, 2025

Language: Английский

Citations

0
Predicting bioavailability of potentially toxic elements (PTEs) in sediment using various machine learning (ML) models: A case study in Mahabad Dam and River-Iran DOI
Fateme Rezaei, Meisam Rastegari Mehr, Ata Shakeri

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 366, P. 121788 - 121788

Published: July 15, 2024

Language: Английский

Citations

3
Long-term natural streamflow forecasting under drought scenarios using data-intelligencw modeling DOI Creative Commons

Lavínia D. Balthazar,

Félix A. Miranda,

Vinícius B.R. Cândido

et al.

Water Cycle, Journal Year: 2024, Volume and Issue: 5, P. 266 - 277

Published: Jan. 1, 2024

Long-term river streamflow prediction and modeling are essential for water resource management decision-making related to resources. This research paper considers the importance of these predictions proposes a model address scarcity scenarios support in allocation, flood management, drought scenarios. Machine learning (ML) techniques offer promising alternatives improving long-term prediction. However, most existing studies on ML models have focused shorter time horizons, limiting their broader applicability. Consequently, there is need dedicated that addresses use Considering this gap, presents an ML-based approach learns replicates natural flow dynamics river, allowing simulation reduced (25% 50% reduction). capability allows simulating varying severity, providing valuable insights service managers. study significantly contributes progress predicting through application machine models. Moreover, offers recommendations hydrologists improve future efforts.

Language: Английский

Citations

2
Machine learning models with innovative outlier detection techniques for predicting heavy metal contamination in soils DOI
Ram Proshad, S Asha,

Rong Kun Jason Tan

et al.

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 481, P. 136536 - 136536

Published: Nov. 19, 2024

Language: Английский

Citations

2
Assessing petrochemical effluent effect on heavy metal pollution in Musa Estuary: A numerical modeling approach DOI
Mohammad Javad Jourtani, Ahmad Shanehsazzadeh, Hossein Ardalan

et al.

Marine Pollution Bulletin, Journal Year: 2024, Volume and Issue: 201, P. 116201 - 116201

Published: March 7, 2024

Language: Английский

Citations

1