Logo Lanfrica

Leveraging novel machine learning models in predicting groundwater irrigation suitability in southeastern Nigeria: A hydrogeochemical approach

Record type:

paper
Creator:
ObiPatSolEmi
Publisher:
Elsevier BV
Host:

Licenses

https://www.elsevier.com/tdm/userlicense/1.0/https://www.elsevier.com/legal/tdmrep-licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/

Similar

Exploring Machine Learning Models in Predicting Irrigation Groundwater Quality Indices for Effective Decision Making in Medjerda River Basin, Tunisia

Over the last years, the global application of machine learning (ML) models in groundwater quality s

Groundwater Quality Evaluation of Fractured Aquifers Using Machine Learning Models and Hydrogeochemical Approaches to Sustainable Water-Irrigation Security in Arid Climate (Central Tunisia)

The primary aims of this research paper involve the creation and verification of machin

Hydrogeochemical Assessment of Groundwater in Epe (Nigeria) for Drinking and Irrigation Purposes

Groundwater sustainability demands routine quality monitoring. This study assessed the hydrogeochemi

Hydrogeochemical and Spatial Assessments of Groundwater Suitability for Drinking and Irrigation in Bazo River Catchment, Rift Valley, Ethiopia

Groundwater is one of the basic requirements for life, economic and social developments in the Bazo

Physicochemical Parameters and Suitability for Irrigation of Groundwater Resources from Biskra Area (Southeastern Algeria)

This study is part of the more general framework for diagnosis of the quality of water resources in

Predicting Crude Oil Price in Nigeria with Machine Learning Models

Background: The rise in crude oil prices yields serious consequences for both oil-producing and non-