Estimation of instantaneous peak flows in Canadian rivers: an evaluation of conventional, nonlinear regression, and machine learning methods

Muhammad Naveed Khaliq
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Abstract

Instantaneous peak flows (IPFs) are often required to derive design values for sizing various hydraulic structures, such as culverts, bridges, and small dams/levees, in addition to informing several water resources management-related activities. Compared to mean daily flows (MDFs), which represent averaged flows over a period of 24 h, information on IPFs is often missing or unavailable in instrumental records. In this study, conventional methods for estimating IPFs from MDFs are evaluated and new methods based on the nonlinear regression framework and machine learning architectures are proposed and evaluated using streamflow records from all Canadian hydrometric stations with natural and regulated flow regimes. Based on a robust model selection criterion, it was found that multiple methods are suitable for estimating IPFs from MDFs, which precludes the idea of a single universal method. The performance of machine learning-based methods was also found reasonable compared to conventional and regression-based methods. To build on the strengths of individual methods, the fusion modeling concept from the machine learning area was invoked to synthesize outputs of multiple methods. The study findings are expected to be useful to the climate change adaptation community, which currently heavily relies on MDFs simulated by hydrologic models.
加拿大河流瞬时峰值流量估算:传统、非线性回归和机器学习方法评估
瞬时峰值流量(IPFs)通常需要用于推导设计值,以确定涵洞、桥梁和小型水坝/堤坝等各种水力结构的大小,此外还可为一些与水资源管理相关的活动提供信息。日平均流量 (MDF) 代表 24 小时内的平均流量,与之相比,仪器记录中往往缺少或无法获得 IPF 的信息。本研究评估了根据 MDFs 估算 IPFs 的传统方法,提出了基于非线性回归框架和机器学习架构的新方法,并利用加拿大所有具有自然和管制流量制度的水文站的流量记录进行了评估。基于稳健的模型选择标准,研究发现多种方法都适用于从 MDFs 估算 IPFs,这就排除了单一通用方法的想法。与传统方法和基于回归的方法相比,基于机器学习的方法的性能也比较合理。为了发挥单个方法的优势,我们引用了机器学习领域的融合建模概念来综合多种方法的输出结果。目前,气候变化适应界在很大程度上依赖于水文模型模拟的 MDFs,预计研究结果将对这一领域有所帮助。
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