PhillyRiverCast: A Real-Time Bacteria Forecasting Model and Web Application for the Schuylkill River

Maimone, Mark; Crockett, Christopher S.; Cesanek, William E.

doi:10.1061/(asce)0733-9496(2007)133:6(542)

Cited by 10 publications

(2 citation statements)

References 7 publications

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“…The Philly Rivercast [ 82 ] provides nowcasts for the Skullykill River and it’s development was outlined by Maimone et al . [ 49 ]. These platforms are used by beach managers and the public, which allows authorities to make real-time water quality decisions easily, and the pubic to learn about beach postings prior to arrival and make decisions about whether or not to swim or engage in other recreational activities at the beach.…”

Section: Discussionmentioning

confidence: 99%

Systematic review of predictive models of microbial water quality at freshwater recreational beaches

et al. 2021

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Monitoring of fecal indicator bacteria at recreational waters is an important public health measure to minimize water-borne disease, however traditional culture methods for quantifying bacteria can take 18–24 hours to obtain a result. To support real-time notifications of water quality, models using environmental variables have been created to predict indicator bacteria levels on the day of sampling. We conducted a systematic review of predictive models of fecal indicator bacteria at freshwater recreational sites in temperate climates to identify and describe the existing approaches, trends, and their performance to inform beach water management policies. We conducted a comprehensive search strategy, including five databases and grey literature, screened abstracts for relevance, and extracted data using structured forms. Data were descriptively summarized. A total of 53 relevant studies were identified. Most studies (n = 44, 83%) were conducted in the United States and evaluated water quality using E. coli as fecal indicator bacteria (n = 46, 87%). Studies were primarily conducted in lakes (n = 40, 75%) compared to rivers (n = 13, 25%). The most commonly reported predictive model-building method was multiple linear regression (n = 37, 70%). Frequently used predictors in best-fitting models included rainfall (n = 39, 74%), turbidity (n = 31, 58%), wave height (n = 24, 45%), and wind speed and direction (n = 25, 47%, and n = 23, 43%, respectively). Of the 19 (36%) studies that measured accuracy, predictive models averaged an 81.0% accuracy, and all but one were more accurate than traditional methods. Limitations identifed by risk-of-bias assessment included not validating models (n = 21, 40%), limited reporting of whether modelling assumptions were met (n = 40, 75%), and lack of reporting on handling of missing data (n = 37, 70%). Additional research is warranted on the utility and accuracy of more advanced predictive modelling methods, such as Bayesian networks and artificial neural networks, which were investigated in comparatively fewer studies and creating risk of bias tools for non-medical predictive modelling.

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Section: Discussionmentioning

confidence: 99%

Systematic review of predictive models of microbial water quality at freshwater recreational beaches

et al. 2021

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“…Therefore, the model software was implemented as a standalone real-time program that runs outside the process space of the web application. The selected water-quality model PhillyRiverCast (Maimone et al, 2007) uses real-time turbidity, flow and rainfall data to provide public service information on the estimated current level of E. coli concentrations in the river and advise on the acceptable types of recreation based on those conditions. The prediction model was customised for the Iowa River to comprise a real-time data collection and modelling program written in Java, data file mapping with SDL and visualisation of the model's output in Dash, as illustrated in Figure 6.…”

Section: Model Integrationmentioning

confidence: 99%

Digital catchment inception using community project components

Muste¹,

Kim²,

Arnold³

et al. 2010

Proceedings of the Institution of Civil Engineers - Water Manag

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It is becoming recognised that synergistic integration of knowledge and methods from a variety of allied disciplines is needed to study, engineer and manage water resources. Until recently this comprehensive investigative approach was limited, in part due to a lack of appropriate technologies. Recent advances in computer, communication and information technologies have led to increasingly pervasive and sophisticated ‘cyberinfrastructure', which enables integration in a common digital environment using remote and in situ measurements with geotemporal databases and high-performance computational models. This paper describes the initial efforts carried out by a University of Iowa research group in implementing and customising software components created by a hydrologic community project to support a local digital catchment. The initial goal was to test what types of data queries the digital catchment can handle and to see how it performs in cases where data streams are coupled with models for continuous forecasting of river water quality. The paper also discusses the general context for digital catchment development and summarises the lessons learned during this initial developmental stage. Given the uniform and scalable nature of the community project components, the workflows described are transferable to other users and other catchments. This synergistic initial effort led to the conclusion that community project components can successfully underpin the national effort of creating a network of ecohydrologic observatories.

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Hydrometeorological variables predict fecal indicator bacteria densities in freshwater: data-driven methods for variable selection

Jones

Liu

Dorevitch

2012

Environ Monit Assess

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Statistical models of microbial water quality inform risk management for water recreation. Current research focuses on resource-intensive, location-specific data collection and water quality modeling, but this approach may be cost-prohibitive for risk managers responsible for numerous recreation sites. As an alternative, we tested the ability of two data-driven models, tree regression and random forests with conditional inference trees, to select readily available hydrometeorological variables for use in linear mixed effects (LME) models predicting bacterial density. The study included the Chicago Area Waterway System (CAWS) and Lake Michigan beaches and harbors in Chicago, Illinois, at which Escherichia coli and enterococci were measured seasonally in 2007-2009. Tree regression node variables reduced data dimensionality by >50 %. Variable importance ranks from random forests were used in a forward-step selection based on R (2) and root mean squared prediction error (RMSPE). We found two to three variables explained bacteria densities well relative to random forests with all variables. LME models with tree- or forest-selected variables performed reasonably well (0.335 < R (2) < 0.658). LME models for Lake Michigan had good prediction accuracy with respect to the single sample maximum standard (72-77 %), but limited sensitivity (23-62 %). Results suggest that our alternative approach is feasible and performs similarly to more resource-intensive approaches.

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PhillyRiverCast: A Real-Time Bacteria Forecasting Model and Web Application for the Schuylkill River

Cited by 10 publications

References 7 publications

Systematic review of predictive models of microbial water quality at freshwater recreational beaches

Systematic review of predictive models of microbial water quality at freshwater recreational beaches

Digital catchment inception using community project components

Hydrometeorological variables predict fecal indicator bacteria densities in freshwater: data-driven methods for variable selection

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