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使用多源遥感和地面测量降雨数据的超集团基于流量流动模拟

Eyob Betru Wegayehu¹, Fiseha Behulu Muluneh¹

¹School of Civil and Environmental Engineering, Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa, Ethiopia.

|July 14, 2023

概括

与单个模型相比,超级组合模型显著改善了每日流量预测. 遥感数据,特别是植被指数,显示了未经老化的水域的巨大潜力,优于传统方法.

科学领域:

水文学的水文学
环境科学环境科学
数据科学数据科学数据科学

背景情况:

传统的流量预测模型在不同的条件下显示不一致的性能.
模型组合和数据合并是水文建模中的新兴趋势.

研究的目的:

为了比较三个超级集体学习者的表现,与八个基础模型进行日常流动模拟.
评估不同输入场景 (包括遥感数据和特征选择) 对流量预测准确性的影响.

主要方法:

利用了12年的每日时间序列数据,来自埃塞俄比亚三个河流流域.
应用了五种输入场景:植被指数,遥感降雨量,地面测量降雨量,融合输入量和递归特征消除 (RFE) 选定的输入量.
使用根平均平方误差 (RMSE),R2,平均绝对误差 (MAE) 和中位数绝对误差 (MEDAE) 评估模型性能.

主要成果:

超级组合学习者始终表现优于稳定表现的基础模型.
与线性回归基线相比,WASE超整体模型的预测得到了13.3%的改进.
XGBoost,CNN-GRU和LSTM是表现最高的基准模型,XGBoost在内部冗余控制后表现出卓越的性能.

结论:

超级组合模型比单个模型提供更可靠的流量预测.
基于遥感的植被指数对于未经成熟的水域中数据驱动的流水流量建模非常有价值.
功能选择,特别是XGBoost中的内部控制,提高了模型性能和稳定性.

关键词:

埃塞俄比亚河流盆地标尺-降雨量标尺-降雨量遥感是一种远程传感.流量预测预测流量预测.超级组合学习就是超级组合学习.

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