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每日河流流量模拟使用集体脱节聚合M5-Prime模型
Khabat Khosravi1, Nasrin Attar2, Sayed M Bateni3,4
1Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, Canada.
Heliyon
|December 6, 2024
概括
这项研究提高了使用M5 Prime (M5P) 组合的河流预测. 分离聚合M5P模型在预测每日河流流量和一天和两天前的流量方面取得了卓越的准确性.
科学领域:
- 水文建模 水文建模
- 水资源管理水资源的管理.
- 环境科学环境科学
背景情况:
- 准确的每日河流流量预测对于减轻洪水和水资源管理至关重要.
- 现有的水文模型在精确预测河流流量方面面临挑战.
- 需要先进的预测模型来提高预测的准确性和可靠性.
研究的目的:
- 引入和评估一个先进的M5 Prime (M5P) 预测模型,用于估计每天的河流流量 (Q) 和预测一天和两天前的河流流量 (Q+1,Q+2).
- 评估M5P合集的性能,包括各种聚合技术,包括引导聚合 (BA),分离聚合 (DA),添加回归 (AR),投票 (V),代分类器优化器 (ICO),随机子空间 (RS) 和旋转森林 (ROF).
- 分析诸如降水 (P) 和蒸发 (Et) 等输入变量对预测准确度的影响以及预测地平线对模型性能的影响.
主要方法:
- 应用M5 Prime (M5P) 预测模型,包括组合变化 (BA,DA,AR,V,ICO,RS,ROF).
- 利用美国Tuolumne县的数据集,包括测量的降水量 (P),蒸发量 (Et) 和河流量 (Q).
- 探索了用于预测Q,Q+1和Q+2的各种输入场景,使用Nash-Sutcliff效率和根平均平方误差等指标评估模型性能.
主要成果:
- 降水 (P) 和河流 (Q) 被确定为影响预测准确性的重要因素.
- 仅仅依靠最相关的变量 (例如,Q) 并不能确保对未来流量的可靠预测.
- 分离聚合M5P (DA-M5P) 模型表现出卓越的性能,纳什-萨特克利夫效率为0.916和RMSE为23 m3/s.
- 组合M5P模型比独立M5P的预测能力提高了1.2%-22.6%.
结论:
- 集成的M5P建模框架显著提高了独立的M5P算法的预测能力,用于河流流量预测.
- DA-M5P模型在准确的每日河流流量估计和短期预测方面表现出卓越的效率.
- 这些发现强调了先进的合并建模技术在改善水资源管理和洪水减缓的水文预测方面的潜力.
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