一个基于人工智能的模型,用于表面和地下水资源的最佳联合运行
Saeid Akbarifard1,2, Mohamad Reza Madadi3, Mohammad Zounemat-Kermani4
1Department of Water Engineering, Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, P.O. Box 76315116, Kerman, Iran. s.akbarifard@kgut.ac.ir.
Nature communications
|January 16, 2024
概括
一个新的混合模型优化了地表和地下水资源管理,实现了超过99%的需求满足和最高的可持续性指数. 这种先进的模拟优化方法优于现有的可靠水资源联合运营方法.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 计算智能是一种计算智能.
背景情况:
- 对地表和地下水资源的综合管理对于水源安全至关重要.
- 现有的模拟优化模型在效率和可持续性方面存在局限性.
- 超启发式算法为复杂的优化问题提供了潜在的可能性.
研究的目的:
- 开发一种新的混合模拟-优化模型,以实现表面和地下水资源的最佳联合运行.
- 提高水资源管理政策的效率和可持续性.
- 将拟议模型的性能与现有的第一级模型进行比较.
主要方法:
- 通过整合群算法和共生组织搜索算法,开发了一种二级混合模型.
- 混合元启发与人工神经网络模拟器相结合.
- 该模型的效率通过将其结果与两个第一级模拟优化模型进行比较来评估.
主要成果:
- 拟议的混合型号可靠地满足了研究地区99%以上的总需求.
- 与两种一级模型相比,开发的模型显示出更高的效率.
- 提出的模型实现了最高的可持续性指数,证实了其有效性.
结论:
- 混合模拟-优化模型为地表和地下水资源的联合运营提供了一种优越的方法.
- 该模型能够满足高需求百分比并实现高可持续性指数的能力突显了其实际适用性.
- 这项研究证实了开发的二级模型在水资源管理中优于先前开发的模型的优势.
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