Integrating machine learning and multi-linear regression modeling approaches in groundwater quality assessment around Obosi, SE Nigeria
- 28 September 2022
- journal article
- research article
- Published by Springer Nature in Environment, Development and Sustainability
- Vol. 25 (12) , 14567-14606
Abstract
No abstract availableKeywords
This publication has 31 references indexed in Scilit:
- Efficient Water Quality Prediction Using Supervised Machine LearningWater, 2019
- Evaluation of Extreme Dry and Wet Conditions Using Climate and Hydrological Indices in the Upper Part of the Gallikos River BasinProceedings, 2018
- Qualitative characterization of groundwater sources around Nigeria National Petroleum Cooperation Oil Depot Aba, using multiple linear regressions modellingInternational Journal of Advanced Geosciences, 2017
- Application of artificial intelligence for the management of landfill leachate penetration into groundwater, and assessment of its environmental impactsJournal of Cleaner Production, 2017
- Uncertainty Considerations in Calibration and Validation of Hydrologic and Water Quality ModelsTransactions of the ASABE, 2015
- Analysis of heavy metals’ contamination in soils and water at automobile junk markets in Obosi and Nnewi, Anambra State, Southeastern NigeriaArabian Journal of Geosciences, 2015
- Evaluation of water quality pollution indices for heavy metal contamination monitoring: a case study from Curtin Lake, Miri City, East MalaysiaEnvironmental Earth Sciences, 2012
- Assessment of heavy metal enrichment factors and the degree of contamination in marine sediments from Tamaki Estuary, Auckland, New ZealandEnvironmental Monitoring and Assessment, 2007
- Status of Heavy Metals in Water and Bed Sediments of River Gomti – A Tributary of the Ganga River, IndiaEnvironmental Monitoring and Assessment, 2005
- Assessing heavy metal contamination in Sado Estuary sediment: An index analysis approachEcological Indicators, 2005