Extent of detection of hidden relationships among different hydrological variables during floods using data-driven models
Extent of detection of hidden relationships among different hydrological variables during floods using data-driven models
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DOI：
10.1007/s10661-021-09499-9
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发表时间：
2021-11-01
影响因子：
3
通讯作者：
Bahreinimotlagh, Masoud
中科院分区：
文献类型：
--
作者：
Al Sawaf, Mohamad Basel;Kawanisi, Kiyosi;Bahreinimotlagh, Masoud
对洪水动力学的理解构成了领先的水资源管理和减轻洪水风险实践的基础。特别是，在大规模洪水事件中准确预测河流流量和捕获河流水位流量的滞后行为是水文研究的关键兴趣之一。文献表明，数据驱动的模型在识别因变量之间的复杂和隐藏的关系方面非常重要，而无需考虑明确的物理方案。在这方面，我们的目标是发现数据驱动的模型可以在多大程度上识别不同水文变量之间的隐藏关系，以生成准确的预测河流流量。第二个目标是检测数据驱动模型是否可以消化训练输入的内部特征，从而推断出训练域之外的严重洪水记录。为了实现这些目标，我们开发了一个递归神经网络（RNN）模型的两个隐藏层捕捉输入之间的隐藏关系，并研究了模型的预测能力，使用定量和定性分析。定量分析包括模型预测之间的比较，以及通过先进的水声系统获得的另一组精确的独立记录作为参考。采用定性的方法，可视化的滞后行为的水位流量关系的模型记录，与高分辨率记录的水声系统。研究结果显示了数据驱动模型在准确预测河流流量方面的潜力。因此，定性分析显示，与参考记录相比，阶段流量回路的相关性中等。此外，还根据东亚季风和热带气旋产生的严重破坏性洪水记录对该模型进行了测试。研究结果表明，数据驱动模型无法推断出训练数据集之外的新特征。总体而言，本研究讨论了RNN在洪水期间提供可靠和准确的河流流量预测的能力。
Understanding of flood dynamics forms the basis for the leading water resource management and flood risk mitigation practices. In particular, accurate prediction of river flow during massive flood events and capturing the hysteretic behavior of river stage-discharge are among the key interests in hydrological research. The literature demonstrates that data-driven models are significant in identifying complex and hidden relationships among dependent variables, without considering explicit physical schemes. In this regard, we aim to discover the extent to which data-driven models can recognize the hidden relationships among different hydrological variables, in order to generate accurate predictions of the river flow. A secondary aim involves the detection of whether data-driven models can digest the internal features of training inputs to extrapolate severe flood records beyond the training domain. To achieve these aims, we developed a recurrent neural network (RNN) model of two hidden layers to capture the hidden relationships among the inputs, and investigated the model's predictive capability using quantitative and qualitative analyses. The quantitative analysis comprised of a comparison between model predictions, and another set of precise independent records obtained through an advanced hydroacoustic system for reference. A qualitative approach was adopted to visualize the hysteretic behavior of the stage-discharge relations of the model records, with the high-resolution records of the hydroacoustic system. The findings display the potential of data-driven models for accurately predicting river flow. Consequently, the qualitative analysis revealed moderate correlations of stage-discharge loops as compared to the reference records. Additionally, the model was tested against severe destructive flood records generated from the East Asian monsoon and tropical cyclones. Its findings suggest that data-driven models cannot extrapolate new features beyond their training dataset. Overall, this study discusses the competence of RNNs in providing reliable and accurate river flow predictions during floods.