基于神经网络的高分辨率气象雷达预报水文模型

L. B. Santos, C. Freitas, L. Bacelar, Jaqueline A. J. P. Soares, Michael M. Diniz, G. R. T. Lima, S. Stephany
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引用次数: 1

摘要

在小而陡峭的流域,许多水文气象灾害发展迅速,对人类生活和基础设施造成重大影响。高分辨率降雨数据和机器学习方法已被用作预测这些事件(如山洪暴发)的建模框架。然而,一个关键的问题仍然存在:降雨量输入数据必须持续多长时间才能进行基于经验的水文预报?本文采用人工神经网络(ANN)水文模型来解决这一问题,以预测河流水位并研究其对先前降雨条件的依赖性。这些试验是在巴西里约热内卢山区的一个小流域利用观测到的水位数据和高分辨率气象雷达降雨估计进行的。因此,只有当数据输入考虑了至少2小时的累积降雨量时,预测水位时间序列才获得了成功的表现(即Nash-Sutcliffe模型效率系数(NSE) > 0.6),这表明与流域集中时间有很强的物理关联。在长时间的累积降雨(>12 h)下,该框架达到了相当高的性能水平(即NSE > 0.85),这可能与人工神经网络捕捉地下响应以及流域过去土壤湿度状态的能力有关。此外,我们研究了模型的鲁棒性,考虑了随机数生成的不同种子,以及空间适用性,查看了权重图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Neural Network-Based Hydrological Model for Very High-Resolution Forecasting Using Weather Radar Data
Many hydro-meteorological disasters in small and steep watersheds develop quickly and significantly impact human lives and infrastructures. High-resolution rainfall data and machine learning methods have been used as modeling frameworks to predict those events, such as flash floods. However, a critical question remains: How long must the rainfall input data be for an empirical-based hydrological forecast? The present article employed an artificial neural network (ANN)hydrological model to address this issue to predict river levels and investigate its dependency on antecedent rainfall conditions. The tests were performed using observed water level data and high-resolution weather radar rainfall estimation over a small watershed in the mountainous region of Rio de Janeiro, Brazil. As a result, the forecast water level time series only archived a successful performance (i.e., Nash–Sutcliffe model efficiency coefficient (NSE) > 0.6) when data inputs considered at least 2 h of accumulated rainfall, suggesting a strong physical association to the watershed time of concentration. Under extended periods of accumulated rainfall (>12 h), the framework reached considerably higher performance levels (i.e., NSE > 0.85), which may be related to the ability of the ANN to capture the subsurface response as well as past soil moisture states in the watershed. Additionally, we investigated the model’s robustness, considering different seeds for random number generating, and spacial applicability, looking at maps of weights.
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