Environmental Hydraulics Institute "IHCantabria", Universidad de Cantabria - Isabel Torres, 15, Parque Científico y Tecnológico de Cantabria, 39011, Santander, Spain.
Water Res. 2019 Mar 1;150:283-295. doi: 10.1016/j.watres.2018.11.063. Epub 2018 Nov 24.
This study aims to provide a method for developing artificial neural networks in estuaries as emulators of process-based models to analyse bathing water quality and its variability over time and space. The methodology forecasts the concentration of faecal indicator organisms, integrating the accuracy and reliability of field measurements, the spatial and temporal resolution of process-based modelling, and the decrease in computational costs by artificial neural networks whilst preserving the accuracy of results. Thus, the overall approach integrates a coupled hydrodynamic-bacteriological model previously calibrated with field data at the bathing sites into a low-order emulator by using artificial neural networks, which are trained by the process-based model outputs. The application of the method to the Eo Estuary, located on the northwestern coast of Spain, demonstrated that artificial neural networks are viable surrogates of highly nonlinear process-based models and highly variable forcings. The results showed that the process-based model and the neural networks conveniently reproduced the measurements of Escherichia coli (E. coli) concentrations, indicating a slightly better fit for the process-based model (R = 0.87) than for the neural networks (R = 0.83). This application also highlighted that during the model setup of both predictive tools, the computational time of the process-based approach was 0.78 times lower than that of the artificial neural networks (ANNs) approach due to the additional time spent on ANN development. Conversely, the computational costs of forecasting are considerably reduced by the neural networks compared with the process-based model, with a decrease in hours of 25, 600, 3900, and 31633 times for forecasting 1 h, 1 day, 1 month, and 1 bathing season, respectively. Therefore, the longer the forecasting period, the greater the reduction in computational time by artificial neural networks.
本研究旨在提供一种在河口开发人工神经网络的方法，将其作为基于过程模型的仿真器，以分析随时间和空间变化的浴场水质及其可变性。该方法通过整合现场测量的准确性和可靠性、基于过程的建模的时空分辨率以及人工神经网络降低计算成本的优势，同时保留结果的准确性，来预测粪大肠菌群的浓度。因此，总体方法将先前在浴场现场数据上校准的耦合水动力-细菌学模型与人工神经网络相结合，通过使用基于过程的模型输出来训练人工神经网络，将其转化为低阶仿真器。该方法在西班牙西北部的 Eo 河口的应用表明，人工神经网络是高度非线性基于过程模型和高度变化的驱动力的可行替代物。结果表明，基于过程的模型和神经网络能够方便地再现大肠杆菌 (E. coli) 浓度的测量值，表明基于过程的模型的拟合度略高于神经网络 (R=0.87 对 R=0.83)。该应用还强调，在这两种预测工具的模型设置过程中，由于人工神经网络开发所需的额外时间，基于过程的方法的计算时间比人工神经网络方法低 0.78 倍。相反，与基于过程的模型相比，神经网络在预测时可以大大降低计算成本，分别减少了 1 小时、1 天、1 个月和 1 个游泳季节预测所需的时间 25、600、3900 和 31633 倍。因此，预测时间越长，人工神经网络降低计算时间的效果越明显。
