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使用基于气候数据和土壤湿度的人工智能模型进行干旱预测
Mhamd Saifaldeen Oyounalsoud1, Abdullah Gokhan Yilmaz2, Mohamed Abdallah3,4
1Department of Civil and Environmental Engineering, University of Sharjah, Sharjah, United Arab Emirates.
Scientific reports
|August 24, 2024
概括
这项研究开发了新的人工智能 (AI) 干旱指数,其性能优于传统方法,可以更准确地预测和监测干旱. 人工智能为更好的干旱评估和减缓战略提供了可靠的方法.
科学领域:
- 环境科学 环境科学
- 气候科学 气候科学
- 数据科学数据科学数据科学
背景情况:
- 干旱对全球经济和社会构成重大风险,需要有效的监测和管理工具.
- 现有的气象干旱指数由于干旱现象的复杂性和不同的水气候条件存在局限性,因此无法普遍应用.
- 普遍适用的干旱指数的缺失凸显了干旱评估中需要先进方法的需要.
研究的目的:
- 开发和评估基于人工智能 (AI) 的新型气象干旱指数,以改进干旱描述和预测.
- 使用历史干旱指标数据,比较人工智能衍生指数与传统干旱指数的表现.
- 在干旱预测中评估人工智能模型的可靠性,包括决策树,通用线性模型,支持矢量机,人工神经网络,深度学习和随机森林.
主要方法:
- 使用决策树 (DT),通用线性模型 (GLM),支持矢量机器,人工神经网络,深度学习和随机森林模型开发基于AI的干旱指数.
- 使用来自澳大利亚爱丽丝斯普林斯的各种气候数据集对人工智能模型进行培训和验证.
- 对人工智能衍生指数与九个常规干旱指数进行比较分析,将它们与历史记录的排水和各种土壤水分水平 (深,低,根,上) 相关联.
主要成果:
- 在传统指数中,降雨异常干旱指数显示了与上层土壤湿度的最高相关性 (0.718).
- 基于DT的AI指数与降雨异常指数的相关性最强 (0.97),而基于GLM的指数与Palmer干旱严重程度指数的相关性最低 (0.57).
- 基于GLM的指数表现出卓越的表现,对上层土壤湿度的相关系数为0.78,表明基于AI的指数通常优于传统的指数.
结论:
- 与传统方法相比,开发的基于AI的干旱指数提供了更准确的干旱预测和监测能力.
- 人工智能为加强干旱评估和减缓战略提供了一个有希望和可靠的方法.
- 该研究强调了AI在解决气象干旱预测复杂性的潜力.
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