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Measurement and analysis of regional water-energy-food nexus resilience with an improved hybrid kernel extreme learning machine model based on a dung beetle optimization algorithm

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  • Zhang, Zhiqin
  • Zhang, Liangliang
  • Liu, Dong
  • Sun, Nan
  • Li, Mo
  • Faiz, Muhammad Abrar
  • Li, Tianxiao
  • Cui, Song
  • Khan, Muhammad Imran

Abstract

In traditional water-energy-food nexus (WEFN) system analysis, the impact of resilience is insufficiently considered, and the accuracy of resilience assessment is low, which can easily lead to system imbalance. Modeling measures can yield optimal evaluation results and provide decision makers with a scientific and efficient basis for management.

Suggested Citation

  • Zhang, Zhiqin & Zhang, Liangliang & Liu, Dong & Sun, Nan & Li, Mo & Faiz, Muhammad Abrar & Li, Tianxiao & Cui, Song & Khan, Muhammad Imran, 2024. "Measurement and analysis of regional water-energy-food nexus resilience with an improved hybrid kernel extreme learning machine model based on a dung beetle optimization algorithm," Agricultural Systems, Elsevier, vol. 218(C).
    • Handle: RePEc:eee:agisys:v:218:y:2024:i:c:s0308521x24001161
    DOI: 10.1016/j.agsy.2024.103966
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    References listed on IDEAS

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    1. Hua, En & Han, Xinxueqi & Engel, Bernard A. & Guan, Jiajie & Sun, Shikun & Wu, Pute & Wang, Bing & Wang, Yubao, 2024. "Developing a sustainable assessment framework for identifying industrial water suitability: Perspective on the water-energy-food nexus," Agricultural Systems, Elsevier, vol. 220(C).

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