相关实验视频

Updated: Jul 28, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

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使用机器学习与马测试预测非多年生河流中的分阶段排放情况

Dinesh Kumar Vishwakarma1, Alban Kuriqi2,3, Salwan Ali Abed4

  • 1Department of Irrigation and Drainage Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, 263145, India.

Heliyon
|May 30, 2023
PubMed
概括

通过机器学习优化分阶段排放等级曲线,特别是线性回归减少误差修剪树 (LR-REPTree) 模型,可显著提高水资源管理和洪水预警的排放估计精度.

关键词:
在GRG技术上,GRG技术后勤回归的逻辑回归机器学习 机器学习评级曲线上的评级曲线.预测分阶段的下放情况.

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