人工智能驱动的预测地质物理河流流与植被的预测
Sanjit Kumar1, Mayank Agarwal1, Vishal Deshpande2
1Indian Institute of Technology Patna, Patna, India.
Scientific reports
|July 16, 2024
概括
混合机器学习模型显著改善了植被道中的河流流速率预测. 添加回归-M5P模型在独立机器学习和实证方法上表现出优异的性能.
科学领域:
- 水文与水资源工程 水文与水资源工程
- 环境流体力学 环境流体力学
- 计算科学 计算科学
背景情况:
- 在植被道中准确预测河流的流速在河流研究中是一个重大挑战.
- 现有的经验方程往往难以捕捉植被影响的复杂动态.
- 机器学习 (ML) 为改善流速预测提供了一个有希望的替代方案.
研究的目的:
- 量化各种独立和混合机器学习 (ML) 模型在植被道中的流速预测性能.
- 将ML模型的有效性与传统的经验方程进行比较.
- 确定影响流速预测的最有影响力的参数.
主要方法:
- 利用了来自自然和实验室流体实验的流速测量.
- 评估了四个独立的ML模型:Kstar,M5P,减少错误修剪树 (REPT) 和随机森林 (RF).
- 评估了八种混合ML算法:AR-Kstar,AR-M5P,AR-REPT,AR-RF,BA-Kstar,BA-M5P,BA-REPT和BA-RF,与添加回归 (AR) 和包装 (BA) 相结合.
主要成果:
- 植被高度被确定为影响流速的最敏感参数.
- 所有评估的ML模型都超过了传统的经验方程.
- 当使用所有输入参数时,大多数ML算法的最佳性能是可以实现的.
- 混合型AR-M5P模型获得了最高的精度 (R2=0.954,R=0.977,NSE=0.954).
结论:
- 混合ML算法提供优越的流速预测在植被河流与独立的ML模型和经验方程相比.
- 推AR-M5P模型作为最佳选择,用于在植被丰富的河流环境中准确预测流速.
- 纳入所有相关的输入参数可以提高ML模型对河流流动力学的预测能力.
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