Optimal decision-making in the water, land and food nexus using artificial intelligence and extreme machine learning

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成果类型：

期刊论文

作者：

Shao, Wei;Ding, Yihang;Wen, Jinghao;Zhu, Pengxu;Ou, Lisong

通讯作者：

Ou, LS

作者机构：

[Shao, Wei] Anhui Univ Technol, Coll Comp Sci & Technol, Maanshan 243000, Anhui, Peoples R China.

[Ding, Yihang] Henan Normal Univ, Coll Comp & Informat Engn, Xinxiang 453000, Henan, Peoples R China.

[Wen, Jinghao] Cent China Normal Univ, Sch Comp Sci, Wuhan 430000, Hubei, Peoples R China.

[Zhu, Pengxu] Univ North Carolina Greensboro, Coll Arts & Sci, Greensboro, NC 27401 USA.

[Ou, Lisong] Guilin Univ Technol, Coll Sci, Guilin 541000, Guangxi, Peoples R China.

通讯机构：

[Ou, LS ] G

Guilin Univ Technol, Coll Sci, Guilin 541000, Guangxi, Peoples R China.

语种：

英文

关键词：

irrigation;production;smart agriculture;sustainable management;wheat

期刊：

Water Supply

ISSN：

1606-9749

年：

2023

卷：

期：

页码：

4166–4177.

DOI：

10.2166/ws.2023.201

基金类别：

This study was supported by the National Natural Science Foundation of China [61906003].

机构署名：

本校为其他机构

院系归属：

计算机学院

摘要：

The development of decision-making systems based on artificial intelligence can lead to achieving optimal solutions water-land-food nexus. In this paper, an extreme learning machine model was developed with the objective function of wheat production maximization. The constraints defined for this problem are divided into three categories: technical parameters of production in agriculture, climatic stress on water resources and land limits. The water, land and food nexus was simulated using 23 experimental farms in Henan province during the 2021–...

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Optimal decision-making in the water, land and food nexus using artificial intelligence and extreme machine learning

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