相关实验视频
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Watershed Planning within a Quantitative Scenario Analysis Framework
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评估地下水力障碍对Qanat流速的影响,使用量子回归森林
Murat Can1, Babak Vaheddoost2, Mir Jafar Sadegh Safari3,4
1State Hydraulic Works of Türkiye, 1 st District, Bursa, Türkiye.
Scientific reports
|November 22, 2025
概括
这项研究使用机器学习来模拟卡纳特排水,发现地下水和蒸发会对水的可用性产生重大影响. 量子回归森林 (QRF) 模型准确地预测了这些复杂的水文相互作用.
科学领域:
- 水文学的水文学
- 水资源管理 水资源管理
- 可持续工程 可持续工程
背景情况:
- 卡纳特是历史悠久的液压系统,对可持续的水资源提取和分配至关重要.
- 了解卡纳特排放动态对于管理水资源至关重要,尤其是在不断变化的环境条件下.
- 最近的改进,如地下水 (SD),改变了传统的Qanat功能.
研究的目的:
- 开发和实施一个数据驱动的建模框架来预测Qanat排放.
- 评估地下水 (SD) 对卡纳特排放潜力的影响.
- 确定影响卡纳特排放的关键水文气象因素.
主要方法:
- 使用量子回归森林 (QRF),随机森林 (RF) 和支持矢量回归 (SVR) 模型.
- 集成的水文气象数据 (降水,温度,蒸发,湿度,排水,透) 和地下水位.
- 引入了一个二进制变量来表示地下水 (SD) 的存在或不存在,以捕捉边界条件的变化.
主要成果:
- 地下水 (SD) 和蒸发被确定为对卡纳特排放最有影响的因素.
- QRF模型表现出强大的预测能力,纳什-萨特克利夫效率 (NSE) 为0.818.
- 该研究捕获了影响卡纳特排水的复杂,非线性水文相互作用.
结论:
- 卡纳特排放受到人类修饰 (SD) 和气候变化 (蒸发) 的重大影响.
- 开发的QRF模型在改变的地下条件下,在预测卡纳特泄漏方面表现出高准确度.
- 这项研究为干旱和半干旱地区的可持续水资源管理提供了宝贵的见解,利用传统的卡纳特系统.
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