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Letter to the Editor “Design of an integrated climatic assessment indicator (ICAI) for wheat production: A case study in Jiangsu Province, China”

Mohammadi, Babak LU orcid (2019) In Ecological Indicators p.493-493
Abstract
In recent years, artificial intelligence techniques such as artificial neural networks (ANN) and Support Vector Regression (SVM) have been well documented in ecological sciences. These methods can perfectly model complex and nonlinear structures, as well as with high processing power and quick computations in ecological sciences. Research on ecological issues with artificial intelligence methods can be useful and provided that the details of the use of these methods are necessary for readers. In this discussion, the discusser has tried to clarify the process of the paper of “Design of an integrated climatic assessment indicator (ICAI) for wheat production: A case study in Jiangsu Province, China” (doi: 10.1016/j.ecolind.2019.01.059). The... (More)
In recent years, artificial intelligence techniques such as artificial neural networks (ANN) and Support Vector Regression (SVM) have been well documented in ecological sciences. These methods can perfectly model complex and nonlinear structures, as well as with high processing power and quick computations in ecological sciences. Research on ecological issues with artificial intelligence methods can be useful and provided that the details of the use of these methods are necessary for readers. In this discussion, the discusser has tried to clarify the process of the paper of “Design of an integrated climatic assessment indicator (ICAI) for wheat production: A case study in Jiangsu Province, China” (doi: 10.1016/j.ecolind.2019.01.059). The discusser would like to call attention to some important points, which may be taken into consideration by the authors and other potential researchers.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Agro-meteorological indicator, support vector machine, random forest, machine learning
in
Ecological Indicators
pages
493 - 493
publisher
Elsevier
external identifiers
  • scopus:85064411355
ISSN
1470-160X
DOI
10.1016/j.ecolind.2019.04.055
language
English
LU publication?
yes
id
d7cb9a6a-93be-4b2c-bc50-5ee07618e294
date added to LUP
2022-09-18 08:21:37
date last changed
2024-02-07 11:29:13
@article{d7cb9a6a-93be-4b2c-bc50-5ee07618e294,
  abstract     = {{In recent years, artificial intelligence techniques such as artificial neural networks (ANN) and Support Vector Regression (SVM) have been well documented in ecological sciences. These methods can perfectly model complex and nonlinear structures, as well as with high processing power and quick computations in ecological sciences. Research on ecological issues with artificial intelligence methods can be useful and provided that the details of the use of these methods are necessary for readers. In this discussion, the discusser has tried to clarify the process of the paper of “Design of an integrated climatic assessment indicator (ICAI) for wheat production: A case study in Jiangsu Province, China” (doi: 10.1016/j.ecolind.2019.01.059). The discusser would like to call attention to some important points, which may be taken into consideration by the authors and other potential researchers.<br/><br/>    Previous article in issue}},
  author       = {{Mohammadi, Babak}},
  issn         = {{1470-160X}},
  keywords     = {{Agro-meteorological indicator; support vector machine; random forest; machine learning}},
  language     = {{eng}},
  pages        = {{493--493}},
  publisher    = {{Elsevier}},
  series       = {{Ecological Indicators}},
  title        = {{Letter to the Editor “Design of an integrated climatic assessment indicator (ICAI) for wheat production: A case study in Jiangsu Province, China”}},
  url          = {{http://dx.doi.org/10.1016/j.ecolind.2019.04.055}},
  doi          = {{10.1016/j.ecolind.2019.04.055}},
  year         = {{2019}},
}