水资源管理中的人工智能,优化和建模技术:相互连接和新兴的协同效应
Hoda S Razavi1, A Pouyan Nejadhashemi1,2, Kalyanmoy Deb3
1Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, Michigan, USA.
概括
本综述审查了水资源管理要素,发现先进的模型和人工智能显示出希望,但面临数据和可扩展性挑战. 整合这些技术可以改善水质保护和资源回收.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 计算水文学计算水文学
背景情况:
- 有效的水资源管理依赖于整合不同的元素,如流域模型,人工智能和决策支持系统.
- 现有的框架往往面临着可扩展性和数据一致性的挑战,限制了最佳的水资源利用.
研究的目的:
- 探索六个关键水资源管理要素之间的相互关系:流域模型,优化算法,人工智能,代用模型,监测和决策支持系统.
- 确定增强水资源管理实践和改善水质保护的潜在协同效应.
主要方法:
- 系统性文献审查遵循系统性审查和元分析 (PRISMA) 准则的首选报告项目.
- 识别,选,资格评估和选择相关研究与交叉引用.
主要成果:
- 先进的流域模型提供了洞察力,但与时空变化和可扩展性作斗争.
- 机器学习方法显示出潜力,但由于数据不足和不一致而受到限制.
- 建立了元素之间的相互联系,但尚未探索的协同效应需要进一步研究.
结论:
- 将新兴技术与已建立的水资源管理框架相结合,对于推进水质保护至关重要.
- 解决数据限制和可扩展性挑战对于最大限度地发挥人工智能和水资源管理先进建模的好处至关重要.
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