对于有效的人工智能技术来评估灌水质量:一个系统的文献审查
Musa Mustapha1, Mhamed Zineddine2, Oluwadamilare Harazeem Abdulganiyu3
1School of Digital Engineering and Artificial Intelligence, Euro-Mediterranean University of Fes, Fes, Morocco. m.musa@ueuromed.org.
Environmental science and pollution research international
|November 20, 2025
概括
人工智能 (AI) 提高了用于可持续农业的灌水质量 (IWQ) 评估. 本综述综合了人工智能应用,强调了改善水资源管理的趋势和挑战.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 计算机科学 计算机科学
背景情况:
- 对可持续农业用水管理的需求不断增加,需要有效的灌水质量 (IWQ) 评估.
- 之前对AI在IWQ评估中的审查缺乏方法论严谨性和全面的综合.
- 系统审查遵循系统审查和元分析 (PRISMA) 准则的首选报告项目.
研究的目的:
- 系统地审查和综合人工智能 (AI) 在灌水质量 (IWQ) 评估中的应用.
- 确定AI驱动的IWQ评估中的关键趋势,挑战和研究差距.
- 为推进可持续的农业水资源管理提供见解.
主要方法:
- 在主要数据库中进行广泛的文献搜索.
- 根据严格的标准,对43篇高质量的文章进行选和选择.
- 分析人工智能技术的趋势,模型集成和应对数据挑战.
主要成果:
- 在IWQ评估中越来越多地采用混合和整体AI模型.
- 越来越重视模型的可解释性和与GIS和物联网的集成.
- 新型人工智能对数据稀缺和阶级不平衡的方法,但由于局部化数据,模型的概括性有限.
结论:
- 人工智能显示出促进IWQ评估和可持续农业的巨大潜力.
- 挑战包括缺乏标准化的IWQ指数和实时监控系统.
- 未来的研究应该专注于提高模型通用性和开发标准化框架.
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