实时流量预测:人工智能与水文洞察

IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY Journal of Hydrology X Pub Date : 2021-12-01 DOI:10.1016/j.hydroa.2021.100110
Witold F. Krajewski , Ganesh R. Ghimire , Ibrahim Demir , Ricardo Mantilla
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引用次数: 17

摘要

在本文中,我们提出了一套简单的基准,用于评估实时流量预测的基于数据的模型,例如使用复杂的人工智能(AI)算法开发的模型。基准测试也是基于数据的,并提供上下文来判断来自更复杂方法的性能指标的增量改进。基准包括时间和空间持久性、基流和径流校正持久性以及从时空分布降雨中获得的河流距离加权径流。在基准的开发过程中,我们使用了基本的水文学见解,如河流网络的流量聚集,流域响应的尺度依赖性,水流划分为快流和基流,水的旅行时间,以及流域宽度函数的降雨量平均。这项研究在爱荷华州使用了140个流量测量仪,覆盖了7到37,000平方公里的流域尺度。数据涵盖了17年。这项工作表明,根据几个常用的度量标准,建议的基准可以提供良好的性能。例如,在一半的测试地点,跨年的流量预测在提前一天的时间内实现了0.6或更高的克林-古普塔效率(KGE)得分,20%的案例达到了0.8或更高的KGE。提议的基准很容易实施,并且应该证明对基于数据和基于物理的水文模型和实时数据同化技术的开发人员有用。
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Real-time streamflow forecasting: AI vs. Hydrologic insights

In this paper, we propose a set of simple benchmarks for the evaluation of data-based models for real-time streamflow forecasting, such as those developed with sophisticated Artificial Intelligence (AI) algorithms. The benchmarks are also data-based and provide context to judge incremental improvements in the performance metrics from the more complicated approaches. The benchmarks include temporal and spatial persistence, persistence corrected for baseflow and streamflow, as well as river distance weighted runoff obtained from space-time distributed rainfall. In the development of the benchmarks, we use basic hydrologic insights such as flow aggregation by the river network, scale-dependence in basin response, streamflow partitioning into quick flow and baseflow, water travel time, and rainfall averaging by the basin width function. The study uses 140 streamflow gauges in Iowa that cover a range of basin scales between 7 and 37,000 km2. The data cover 17 years. This work demonstrates that the proposed benchmarks can provide good performance according to several commonly used metrics. For example, streamflow forecasting at half of the test locations across years achieves a Kling-Gupta Efficiency (KGE) score of 0.6 or higher at one-day ahead lead time, and 20% of cases reach the KGE of 0.8 or higher. The proposed benchmarks are easy to implement and should prove useful for developers of data-based as well as physics-based hydrologic models and real-time data assimilation techniques.

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来源期刊
Journal of Hydrology X
Journal of Hydrology X Environmental Science-Water Science and Technology
CiteScore
7.00
自引率
2.50%
发文量
20
审稿时长
25 weeks
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