Predicting faecal indicator levels in estuarine receiving waters – An integrated hydrodynamic and ANN modelling approach

Lin, BinLiang; Syed, Munira; Falconer, Roger Alexander

doi:10.1016/j.envsoft.2007.09.009

Cited by 46 publications

(15 citation statements)

References 16 publications

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“…In addition, typical coastal water parameters, such as tide level and type, were not included in the ANN models. A similar problem was involved in the process-based model developed by Lin et al (2008). The process-based model was calibrated and tested with the data collected in only 50 h from the bathing water of the Ribble Estuary, UK.…”

Section: Independent Testing Results and Discussionmentioning

confidence: 99%

“…The beach boundary layer model provides a fundamental basis for the selection of model parameters and the construction of site-specific models. Lin et al (2008) proposed an integrated hydrodynamic and artificial neural network (ANN) modeling approach to predict fecal coliform levels in estuarine receiving waters. The multiple linear regression (MLR) method has also been widely used in modeling and prediction of beach bacteria concentrations Ge and Frick 2007;Whitman 2008, 2011).…”

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confidence: 99%

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Modeling Fecal Coliform Bacteria Levels at Gulf Coast Beaches

Zhang

Deng

Rusch

2014

Water Qual Expo Health

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This study presents and compares two models for predicting fecal coliform levels at Gulf Coast beaches in Louisiana, USA. One was developed using the artificial neural network (ANN) in MATLAB toolbox and the other one was developed based on the multiple linear regression method (MLR). A total of six independent environmental variables, including rainfall, tide, wind, salinity, temperature, and weather type along with eight different combinations of the independent variables are capable of explaining about 76 % of variation in fecal coliform levels for model training data and 44 % for independent data. The findings are obtained from the ANN model and the MLR model using six years of bacteriological and environmental monitoring data. The results show that the ANN model performs consistently better than the MLR model. Applications of the ANN model can significantly reduce potential health risks of fecal pollution to beachgoers. The paper provides new insights into environmental processes responsible for the variation in levels of fecal coliform bacteria in coastal beach waters as well.

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Section: Independent Testing Results and Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Modeling Fecal Coliform Bacteria Levels at Gulf Coast Beaches

Zhang

Deng

Rusch

2014

Water Qual Expo Health

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“…A similar approach was applied by Frick et al (2005) for Lake Erie, U.S.A. Viegas et al (2012) developed a hygienie early warning system for a beach at the Portuguese coast based on a deterministic modeling approach, taking into account tides, currents, and wind. Another method to predict bathing water quality was proposed by Lin et al (2008) and Kashefipour et al (2005): artificial neural networks (ANNs), which received input data from a hydrodynamic and water quality model. Vinten et al (2004) tested three different model approaches for bathing water quality at a river in Fig.…”

Section: Introductionmentioning

confidence: 99%

Comparison of different model approaches for a hygiene early warning system at the lower Ruhr River, Germany

Mälzer

Beek

Müller³

et al. 2016

International Journal of Hygiene and Environmental Health

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“…However, their level of influence is very complex to predict from firstprinciples as it will greatly depend on understanding water transport processes and land-sea morphologies near the coast [3]. In this case, models can be efficiently constructed using observation data time series to predict the causal effects of microbial contamination in bathing zones [4].…”

Section: Explanatory Processes and Parametersmentioning

confidence: 99%

Open Data Sources for the Development of Mobile Applications and Forecast of Microbial Contamination in Bathing Waters

Correndo

Sabeur

2015

IFIP Advances in Information and Communication Technology

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Abstract. This paper describes a service oriented architecture for mobile and web applications and the enablement of participatory observations of the environment. The architecture hosts generic microbial risk forecast models in bathing zones, which are trained by heterogeneous input data. Open observation data sources, specializing in water quality indicators and environmental processes are used for the construction of such applications. Nevertheless, the encountered integration of the open data sources was challenging due to the various incompatibilities found in data samples. These included gaps in the data with diverse temporal and spatial coverage as well as conflicting collection policies.

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Predicting faecal indicator levels in estuarine receiving waters – An integrated hydrodynamic and ANN modelling approach

Cited by 46 publications

References 16 publications

Modeling Fecal Coliform Bacteria Levels at Gulf Coast Beaches

Modeling Fecal Coliform Bacteria Levels at Gulf Coast Beaches

Comparison of different model approaches for a hygiene early warning system at the lower Ruhr River, Germany

Open Data Sources for the Development of Mobile Applications and Forecast of Microbial Contamination in Bathing Waters

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