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基于物理与数据驱动的模型用于在数据稀缺的半干旱盆地预测溪流和水库体积
Gülhan Özdoğan-Sarıkoç1, Filiz Dadaser-Celik2
1Department of Vegetable and Animal Production, Suluova Vocational School, Amasya University, Amasya, Turkey.
概括
数据驱动的模型在预测数据稀缺的半干旱盆地中的水库容量和流量方面表现优于基于物理的模型. 这表明数据驱动的方法是这些地区水文预测的有价值的替代方案.
科学领域:
- 水文学的水文学
- 环境建模环境建模
背景情况:
- 基于物理和数据驱动的模型用于水文预测.
- 对于数据稀缺的半干旱盆地和改变水文学的有限的比较研究存在.
研究的目的:
- 为了比较物理模型 (SWAT) 和数据驱动模型 (NARX) 的性能,用于预测水库体积和流量.
- 评估这些模型在数据稀缺,半干旱的土耳其特尔萨坎盆地,由于水库而改变了水文制度.
主要方法:
- 采用土壤和水评估工具 (SWAT) 作为基于物理的模型.
- 采用非线性自回归异源 (NARX) 模型作为数据驱动的方法.
- 校准和验证了两种流量和水库体积的模型.
主要成果:
- 与SWAT相比,NARX模型在预测水库体积 (Ladik和Yedikir) 和盆地出水流量方面表现出色.
- 这两种模型在拉迪克水库体积预测方面表现最好,在耶迪基尔水库体积预测方面排名第二.
- 在流域出口的溪流预测中,模型性能最低.
结论:
- 数据驱动模型,如NARX,为物理模型提供了可行的替代方案,特别是在数据稀缺的环境中.
- 基于物理模型的输入数据中的不确定性,例如SWAT,可能会导致预测性能降低.
- 对比研究对于理解模型在复杂水文环境中的适用性至关重要.
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