在数据不足的地区使用DL辅助的,参数优化的水文模型进行现实的每日排放建模
Imee V Necesito1, Junhyeong Lee2, Seonuk Baek2
1Institute of Water Resources System, Inha University, Incheon, South Korea. ivnecesito@inha.ac.kr.
Scientific reports
|December 18, 2025
概括
深度学习 (DL) 模型在水文科学中表现有前途,但存在局限性. 将DL与传统方法相结合的混合模型提供了最可靠的流动模拟,特别是在数据稀缺的地区.
科学领域:
- 水文科学 水文科学
- 深度学习应用程序
- 水资源管理 水资源管理
背景情况:
- 深度学习 (DL) 正在迅速推进水文科学,但其局限性尚未完全理解.
- 与传统和混合方法一起评估DL模型对于流量模拟至关重要,特别是在数据不足的领域.
研究的目的:
- 评估基于DL的,传统的 (HEC-HMS,GR4J) 和混合水文模型的效力,用于流动模拟.
- 为了比较数据稀缺地区的模型性能,专注于峰值和低流量估计.
主要方法:
- 在菲律宾萨马尔的四个分捕获区中模拟每日排放,使用HEC-HMS,单变长短期记忆 (LSTM) 网络,经典GR4J和DL辅助的GR4J.
- 参数优化了DL辅助的GR4J模型.
主要成果:
- 经典GR4J低估了排放;LSTM高估了峰值.
- 通过DL辅助的GR4J表现出卓越的性能 (NSE:0.63-0.84,IA:0.85-0.93),具有平衡的峰值和低流量估计.
- 统一变量LSTM捕获了趋势,但错过了一些高峰,尽管整体指标很高.
结论:
- 无论是独立的DL还是传统的模型都不足以进行最佳的水文模拟.
- DL辅助的混合模型提供了更可靠和更现实的流动模拟,对于数据不足和气候敏感地区至关重要.
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