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Prediction of soil cation exchange capacity using enhanced machine learning approaches in the southern region of the Caspian Sea

Emamgholizadeh, Samad ; Bazoobandi, Ahmad ; Mohammadi, Babak LU orcid ; Ghorbani, Hadi and Amel Sadeghi, Mohammad (2023) In Ain Shams Engineering Journal 14(2).
Abstract

Cation exchange capacity (CEC) has a key role in soil studies such as agriculture, energy balance, characteristics of the soil for food, maintaining water in the soil as well as soil pollution management. Its measurement is difficult and time-consuming. So, its prediction using artificial intelligent (AI) models with soil readily available properties can be the proper solution. In this study, the physical and chemical properties of the soil, such as pH, EC, organic carbon, clay content, sands, and total nitrogen used as input data for the AI models. The adaptive-network-based fuzzy inference system (ANFIS), ANFIS model coupled by differential evolution (ANFIS-DE), and ANFIS model coupled by particle swarm optimization (ANFIS-PSO) are... (More)

Cation exchange capacity (CEC) has a key role in soil studies such as agriculture, energy balance, characteristics of the soil for food, maintaining water in the soil as well as soil pollution management. Its measurement is difficult and time-consuming. So, its prediction using artificial intelligent (AI) models with soil readily available properties can be the proper solution. In this study, the physical and chemical properties of the soil, such as pH, EC, organic carbon, clay content, sands, and total nitrogen used as input data for the AI models. The adaptive-network-based fuzzy inference system (ANFIS), ANFIS model coupled by differential evolution (ANFIS-DE), and ANFIS model coupled by particle swarm optimization (ANFIS-PSO) are used for the prediction of the CEC. Then the ability of those methods in the prediction of the CEC. Results showed higher efficiency of the coupled models (ANFIS-DE and ANFIS-PSO) compared to the ordinary ANFIS model.

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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
artificial intelligence, machine learning, Differential evolution algorithm, Multidisciplinary research, Multiple soil classes, Particle swarm optimization
in
Ain Shams Engineering Journal
volume
14
issue
2
article number
101876
pages
11 pages
publisher
Ain Shams University
external identifiers
  • scopus:85133298210
ISSN
2090-4479
DOI
10.1016/j.asej.2022.101876
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2022 Faculty of Engineering, Ain Shams University
id
ec90e194-5418-4f37-b86b-2358fe92c530
date added to LUP
2022-07-18 22:08:58
date last changed
2024-01-24 10:59:55
@article{ec90e194-5418-4f37-b86b-2358fe92c530,
  abstract     = {{<p>Cation exchange capacity (CEC) has a key role in soil studies such as agriculture, energy balance, characteristics of the soil for food, maintaining water in the soil as well as soil pollution management. Its measurement is difficult and time-consuming. So, its prediction using artificial intelligent (AI) models with soil readily available properties can be the proper solution. In this study, the physical and chemical properties of the soil, such as pH, EC, organic carbon, clay content, sands, and total nitrogen used as input data for the AI models. The adaptive-network-based fuzzy inference system (ANFIS), ANFIS model coupled by differential evolution (ANFIS-DE), and ANFIS model coupled by particle swarm optimization (ANFIS-PSO) are used for the prediction of the CEC. Then the ability of those methods in the prediction of the CEC. Results showed higher efficiency of the coupled models (ANFIS-DE and ANFIS-PSO) compared to the ordinary ANFIS model.</p>}},
  author       = {{Emamgholizadeh, Samad and Bazoobandi, Ahmad and Mohammadi, Babak and Ghorbani, Hadi and Amel Sadeghi, Mohammad}},
  issn         = {{2090-4479}},
  keywords     = {{artificial intelligence; machine learning; Differential evolution algorithm; Multidisciplinary research; Multiple soil classes; Particle swarm optimization}},
  language     = {{eng}},
  number       = {{2}},
  publisher    = {{Ain Shams University}},
  series       = {{Ain Shams Engineering Journal}},
  title        = {{Prediction of soil cation exchange capacity using enhanced machine learning approaches in the southern region of the Caspian Sea}},
  url          = {{http://dx.doi.org/10.1016/j.asej.2022.101876}},
  doi          = {{10.1016/j.asej.2022.101876}},
  volume       = {{14}},
  year         = {{2023}},
}