在降雨-流水模拟中,SWAT和混合M5P树模型的性能比较
Shailesh Kumar1, K K Pandey2, Ajay Ahirwar3
1Department of Civil Engineering, IIT (BHU) Varanasi, Varanasi 221005, Uttar Pradesh, India
Journal of water and health
|April 28, 2024
概括
这项研究使用土壤和水评估工具 (SWAT) 和混合M5P模型树增强了流量预测. 混合M5P树模型在每日流量预测方面表现出卓越的准确性.
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:
- 准确的溪流流量预测对于有效的水资源管理至关重要.
- 传统的水文模型,如土壤和水评估工具 (SWAT),在捕捉复杂的河流流动力学方面存在局限性.
- 机器学习技术的整合有可能提高预测准确度.
研究的目的:
- 评估和比较土壤和水评估工具 (SWAT) 模型和混合M5P模型树的性能,用于每日流量预测.
- 评估使用SWAT的剩余时间序列以通过M5P模型树进行增强的有效性.
- 在Hemvati河上的MH Halli测量站确定最适合用于水文预报的模型.
主要方法:
- 利用了14年的每日流量数据集 (2003-2017年) 来自MH哈利测量站.
- 校准和验证土壤和水评估工具 (SWAT) 模型与观察到的流量数据对比.
- 通过将M5P模型树应用于SWAT模型的剩余时间序列,开发了一种混合方法.
主要成果:
- 混合M5P树模型在每日流量预测中明显优于独立的SWAT模型.
- 评估指标包括根-平均-平方误差,确定系数 (R2),纳什-萨特克利夫效率和一致度 (d) 支持混合模型.
- M5P模型树有效地解决了SWAT预测中的差异,从而提高了准确性.
结论:
- 混合M5P树模型是提高每日流量预测准确性的高效工具.
- 这项研究为优化水文预测和水资源管理策略提供了宝贵的见解.
- 基于性能指标的模型选择对于特定的水文预测应用至关重要.
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