Zubelzu Sergio, Ghalkha Abdulmomen, Ben Issaid Chaouki, Zanella Andrea, Bennis Medhi
Departamento de Ingeniería Agroforestal, Universidad Politécnica de Madrid, Madrid, Spain.
Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland.
J Environ Manage. 2024 Mar;354:120404. doi: 10.1016/j.jenvman.2024.120404. Epub 2024 Feb 19.
In this paper, we present an approach that combines data-driven and physical modelling for predicting the runoff occurrence and volume at catchment scale. With that aim, we first estimated the runoff volume from recorded storms aided by the Green-Ampt infiltration model. Then, we used machine learning algorithms, namely LightGBM (LGBM) and Deep Neural Network (DNN), to predict the outputs of the physical model fed on a set of atmospheric variables (relative humidity, temperature, atmospheric pressure, and wind velocity) collected before or immediately after the beginning of the storm. Results for a small urban catchment in Madrid show DNN performed better in predicting the runoff occurrence and volume. Moreover, enriching the input primary atmospheric variables with auxiliary variables (e.g., storm intensity data recorded during the first hour, or rain volume and intensity estimates obtained from auxiliary regression methods) largely increased the model performance. We show in this manuscript data-driven algorithms shaped by physical criteria can be successfully generated by allowing the data-driven algorithm learn from the output of physical models. It represents a novel approach for physics-informed data-driven algorithms shifting from common practices in hydrological modelling through machine learning.
在本文中，我们提出了一种结合数据驱动和物理建模的方法，用于预测流域尺度上的径流发生情况和径流量。为此，我们首先借助格林 - 安普特入渗模型，根据记录的暴雨估算径流量。然后，我们使用机器学习算法，即轻量级梯度提升机（LightGBM，LGBM）和深度神经网络（DNN），来预测基于一组在暴雨开始前或开始后立即收集的大气变量（相对湿度、温度、大气压力和风速）输入的物理模型的输出。马德里一个小型城市流域的结果表明，DNN在预测径流发生情况和径流量方面表现更好。此外，用辅助变量（例如，第一小时记录的暴雨强度数据，或通过辅助回归方法获得的降雨量和强度估计值）丰富输入的主要大气变量，可大幅提高模型性能。我们在本手稿中表明，通过让数据驱动算法从物理模型的输出中学习，可以成功生成由物理准则塑造的数据驱动算法。它代表了一种有别于水文建模中通过机器学习的常见做法的物理信息数据驱动算法的新方法。
