使用可变和模型生成的合成数据对非线性头部动力学的时间序列分析
Martin A Vonk1,2, Raoul A Collenteur3, Sorab Panday4
1Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, South Holland, The Netherlands.
Ground water
|April 6, 2024
概括
本研究使用合成数据评估时间序列模型来预测地下水头部. 非线性模型准确地模拟了地下水的动态,在水文预测方面表现优于线性模型.
科学领域:
- 水文地质学 水文地质学
- 环境建模环境建模
- 时间序列分析时间序列分析
背景情况:
- 地下水的头部波动受到复杂的水文过程的影响,包括降水和蒸发.
- 精确模拟这些动态对于有效的水资源管理至关重要.
- 现有的时间序列模型可能无法完全捕捉地下水系统的非线性行为.
研究的目的:
- 评估线性和非线性时间序列模型在模拟合成地下水头部数据中的性能.
- 将这些模型的准确性与数值理查德方程模型进行比较.
- 为评估数据驱动的水文模型提供工具.
主要方法:
- 使用数值模型解决理查德斯方程用于变量和流量的合成地下水头系列.
- 在不同的土壤类型和不和区域厚度下,模拟头部对降水和蒸发的反应.
- 应用和评估了使用R平方值的线性和非线性时间序列模型.
主要成果:
- 线性时间序列模型实现了从0.67到0.96.9的R平方值.
- 非线性时间序列模型,结合根区储库,始终在0.9.9以上实现R平方值.
- 非线性模型的降水事件反应与数值模型的输出密切匹配.
结论:
- 与线性模型相比,非线性时间序列模型在模拟地下水头动态方面表现优越.
- 开发的合成数据生成脚本可以用于测试各种数据驱动的水文模型.
- 通过先进的时间序列技术,可以准确模拟地下水充电和头部反应.
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