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使用每小时降雨数据集预测不同尺度的流域的洪水阶段: 大量的降雨具有增强机器学习方法的功能
Lei Qiao1, Daniel Livsey2, Jarrett Wise2
1Oklahoma Water Resources Center, Oklahoma State University, Stillwater, OK 74078, USA.
The Science of the total environment
|August 4, 2024
概括
使用降雨数据和机器学习模型实现了准确的洪水预测. 随机森林回归准确地预测了湖泊洪水阶段和各种流域大小的河流.
科学领域:
- 水文和水资源管理 水文和水资源管理
- 环境工程 环境工程
- 数据科学和机器学习
背景情况:
- 准确预测湖泊水位和洪水流量对于有效的洪水预报至关重要.
- 卡尔·布莱克韦尔湖作为一个案例研究,利用其广泛的历史数据.
- 了解当前和先前降雨对水位的贡献至关重要.
研究的目的:
- 开发和评估准确的方法来预测瞬间的湖泊洪水阶段.
- 评估各种降雨特征的预测能力,包括长期累积的数量.
- 为了比较多个回归算法的性能,用于洪水阶段预测.
主要方法:
- 设计的每小时降雨特征,包括从现在到600小时前的累积量,以及前一天的湖泊水位.
- 采用随机森林回归 (RFR),神经网络多层感知器 (NN-MLP),支持向量回归 (SVR) 和极端梯度增强 (XGBoost).
- 使用线性模型,如多重线性回归 (MLR),主要组件回归 (PCR) 和部分最小平方回归 (PLSR) 进行比较.
主要成果:
- RFR和NN-MLP实现了湖泊洪水阶段的高预测准确性 (R2高达0.95,MAE0.11英尺,RMSE0.21英尺).
- 非线性算法 (XGBoost,SVR) 的表现略低,但表现仍然强.
- 尺寸缩小的线性回归模型表现出最低的精度.
- 该方法证明了其高准确性和适用性,用于在不同流域尺度上对地表流水和溪流的预测.
结论:
- 机器学习模型,特别是RFR和NN-MLP,在使用累积的降雨数据来预测湖泊洪水阶段方面非常有效.
- 降雨数据的预测能力因流域大小而异,较早降雨对较大流域的影响更大.
- 开发的方法提供了一个强大的和广泛适用的解决方案,用于在不同规模的水文预测.
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