评估基于SWAT和支持向量的回归加上离散波形变换的月度流量预测
Lifeng Yuan1,2, Kenneth J Forshay1
1U.S. Environmental Protection Agency, Center for Environmental Solutions and Emergency Response, Robert S. Kerr Environmental Research Center, Ada, OK 74820, USA.
Water
|March 27, 2025
概括
一个新的混合土壤和水评估工具-波形支向量回归 (SWAT-WSVR) 模型改善了有限数据的流域的流量预测准确性. 这种综合方法通过提供更可靠的水文模拟来增强水资源管理.
科学领域:
- 水文学的水文学
- 水资源管理 水资源管理
- 环境建模环境建模
背景情况:
- 准确的流量预测对于有效的流域规划和管理至关重要.
- 有限的数据可用性往往挑战水文模型的精度.
- 像SWAT-CUP和SWAT-SVR这样的现有模型在数据极为稀缺的环境中存在局限性.
研究的目的:
- 开发和评估一种新的混合土壤和水评估工具-波形支向量回归 (SWAT-WSVR) 模型.
- 为了提高流量预测准确度,在水分区的水文数据有限.
- 将SWAT-WSVR模型的性能与传统的SWAT-CUP和SWAT-SVR方法进行比较.
主要方法:
- 土壤和水评估工具 (SWAT) 与支向量回归 (SVR) 和离散波波变换 (DWT) 的集成.
- 利用模拟流量和降水时间序列的波纹组件作为SVR模型的输入.
- 使用统计指标 (RSR,NSE,PBIAS,RMSE),泰勒图和水文图对12个水文遗址的性能评估.
主要成果:
- 在校准过程中,SWAT-WSVR模型表现出卓越的性能,平均RSR为0.02和NSE为1.00.
- 验证结果显示出强的表现,平均RSR为0.14和NSE为0.98.
- 在准确性和减少差异性方面,SWAT-WSVR模型始终超过SWAT-CUP和SWAT-SVR.
结论:
- 开发的SWAT-WSVR模型在流动模拟准确度上有了显著的改进,特别是在数据有限的流域.
- 这种混合方法为增强水文建模提供了有价值的替代校准策略.
- 这些发现支持SWAT-WSVR用于更可靠的水资源规划和管理的应用.
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