Performances of MLR, RBF-NN, and MLP-NN in the evaluation and prediction of water resources quality for irrigation purposes under two modeling scenarios-Reference-Cited by-同舟云学术

Performances of MLR, RBF-NN, and MLP-NN in the evaluation and prediction of water resources quality for irrigation purposes under two modeling scenarios

Published:2022-06-17 Issue:26 Volume:37 Page:14399-14431
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Egbueri Johnbosco C.¹^ORCID,Agbasi Johnson C.¹^ORCID

Affiliation:

1. Department of Geology, Chukwuemeka Odumegwu Ojukwu University, Uli, Nigeria

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2022.2087758

Reference134 articles.

1. River water modelling prediction using multi-linear regression, artificial neural network, and adaptive neuro-fuzzy inference system techniques

2. Evolutionary computational intelligence algorithm coupled with self-tuning predictive model for water quality index determination

3. Application of neural network and time series modeling to study the suitability of drain water quality for irrigation: a case study from Egypt

4. Effect of Marginal-Quality Irrigation on Accumulation of some Heavy Metals (Mn, Pb, and Zn) in TypicTorripsamment Soils and Food Crops

5. Predicting Cadmium Concentrations in Wheat and Barley Grain Using Soil Properties

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