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Updated: May 30, 2025

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基于多种机器学习技术,预测雨园流水控制效应
Xing-Li Jia1, Qi Yang1, Hui Liang2,3
1School of Highway, Chang'an University, Xi'an, People's Republic of China.
Environmental technology
|January 29, 2025
概括
这项研究开发了预测模型,以评估雨水花园在控制城市淹水方面的有效性. 使用斑马优化算法 (Zebra Optimization Algorithm) 的优化BP模型在预测流水控制率方面取得了很高的准确性.
科学领域:
- 环境工程 环境工程
- 城市水文学 城市水文学
- 可持续的城市发展
背景情况:
- 城市化加剧了降雨期间淹水的问题.
- 雨水花园对于管理城市地表排水和减轻水淹没至关重要.
- 了解影响雨园有效性的因素对于城市规划至关重要.
研究的目的:
- 用多种建模方法预测雨水花园的排水控制效率.
- 为了确定影响雨园性能的关键参数.
- 优化预测模型以提高准确性.
主要方法:
- 从五个实验性雨水花园结构中收集了240个排水控制率数据集.
- 进行特征相关性分析以确定关键输入参数 (降雨复发间隔,储存层深度,采水区,透率).
- 使用斑马优化算法 (ZOA) 开发和优化预测模型 (BP,SVM,随机森林).
主要成果:
- 斑马优化算法优化反向传播 (ZOA-BP) 模型显示出优异的预测准确性 (R2 = 0.979).
- ZOA-BP模型实现了2.331的根平均平方误差 (RMSE),验证了它的预测能力.
- 影响流水控制的关键参数被确定并纳入模型.
结论:
- ZOA-BP模型提供了一种可靠的工具,用于预测雨水花园流水控制效率.
- 这项研究为设计和实施有效的雨水园系统提供了宝贵的见解.
- 这些发现有助于降低城市水资源管理项目的设计和运营成本.
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