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

Summary, in English

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.

Previous article in issue

• Author: Babak Mohammadi
• Department/s: Dept of Physical Geography and Ecosystem Science
• Publishing year: 2019
• Language: English
• Pages: 493-493
• Publication/Series: Ecological Indicators
• Links: http://dx.doi.org/10.1016/j.ecolind.2019.04.055
• Document type: Journal article (letter)
• Publisher: Elsevier
• Topic: Physical Geography
• Keywords: Agro-meteorological indicator; support vector machine; random forest; machine learning
• Status: Published
• ISBN/ISSN/Other: ISSN: 1470-160X