DOI: 10.1016/j.watres.2018.11.063
Scopus记录号: 2-s2.0-85057819859
论文题名: Artificial neural networks as emulators of process-based models to analyse bathing water quality in estuaries
作者: García-Alba J. ; Bárcena J.F. ; Ugarteburu C. ; García A.
刊名: Water Research
ISSN: 431354
出版年: 2019
起始页码: 283
结束页码: 295
语种: 英语
英文关键词: Bathing water quality
; Eo estuary
; Escherichia coli (E. coli)
; Hydrodynamic-bacteriological model
; Machine learning
Scopus关键词: Escherichia coli
; Estuaries
; Forecasting
; Hydrodynamics
; Learning systems
; Water quality
; Bathing water
; Escherichia coli (E. coli)
; Faecal indicator organisms
; Process-based approach
; Process-based modeling
; Process-based modelling
; Process-based models
; Spatial and temporal resolutions
; Neural networks
; brackish water
; artificial neural network
; bathing water
; coliform bacterium
; estuary
; hydrodynamics
; machine learning
; spatiotemporal analysis
; water quality
; Article
; artificial neural network
; Escherichia coli
; estuary
; hydrodynamics
; indicator organism
; measurement accuracy
; nonhuman
; priority journal
; process model
; reliability
; Spain
; water quality
; Eo Estuary
; Spain
; Escherichia coli
英文摘要: 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 (R2 = 0.87) than for the neural networks (R2 = 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. © 2018 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/122087
Appears in Collections: 气候变化事实与影响
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作者单位: Environmental Hydraulics Institute “IHCantabria”, Universidad de Cantabria - Isabel Torres, 15, Parque Científico y Tecnológico de Cantabria, Santander, 39011, Spain
Recommended Citation:
García-Alba J.,Bárcena J.F.,Ugarteburu C.,et al. Artificial neural networks as emulators of process-based models to analyse bathing water quality in estuaries[J]. Water Research,2019-01-01