机器学习模型的比较评估,用于在亚热带季风流域的每日流量预测
Zhi Zhang1, Yusha Xiao2, Runting Chen3
1Tourism and Historical Culture College, Zhaoqing University, Zhaoqing, 526061, Guangdong, China. zhangzhi@zqu.edu.cn.
Scientific reports
|February 5, 2026
概括
长短期记忆 (LSTM) 模型在季风流域的流量预测方面表现出色,优于其他机器学习方法,特别是在极端高流量事件和洪水峰值期间.
科学领域:
- 水文学 水文学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 精确的流量预测对于管理水资源和在亚热带季风地区发出洪水警告至关重要.
- 选择最佳的机器学习模型用于流量预测仍然是一个重大挑战.
研究的目的:
- 为了比较每日流量预测的七个机器学习模型的性能.
- 在极端高流量条件下评估模型稳定性,并用于洪水峰值预测.
- 识别关键预测因素,并了解影响河流流动的水文过程.
主要方法:
- 七个机器学习模型的比较分析:线性回归 (LR),梯度增强回归器,人工神经网络 (ANN),随机森林,额外树木回归器,XGBoost (XGB) 和长短期记忆 (LSTM).
- 使用像纳什-萨特克利夫效率 (NSE) 和克林-古普塔效率 (KGE) 这样的指标进行评估.
- 分析特征重要性和残余模式,以了解模型行为和水文影响.
主要成果:
- 与其他模型相比,LSTM表现出优越的性能 (NSE/KGE为0.95).
- 在高流量事件期间,LSTM保持了强大的预测准确性 (NSE 0.86-0.45为>90至99百分位).
- 与以树为基础的模型 (30-50%) 相比,LSTM显著减少了对洪水峰值的低估 (7-20%).
- 上游流被确定为主要的预测因素,突出了流域记忆效应.
结论:
- 在LSTM中实施的时间记忆机制,为流量预测提供了显著的优势,特别是在极端水文条件下.
- 这些发现为在操作洪水预测系统中选择合适的模型提供了宝贵的指导.
- 了解流域记忆和水文条件对于准确的流量预测至关重要.
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