scholarly journals Artificial neural network based nowcasting model for beach water quality

2021 ◽  
Author(s):  
Jainy Mavani
Keyword(s):  
Real Time ◽  
Meteorological Data ◽  
Point Sources ◽  
Indicator Organisms ◽  
E Coli ◽  
Water Users ◽  
Ann Models ◽  
Artificial Neural ◽  
Persistence Model ◽  
Beach Water Quality

Recreational water users may be exposed to elevated pathogen levels that originate from various point and non-point sources. Current daily notifications practice depends on microbial analysis of indicator organisms such as Escherichia coli (E. coli) that require 18-24 hours to provide sufficient response. This research evaluated the use of Artificial Neural Networks (ANNs) for real time prediction of E. coli concentration in water at Toronto beaches (Ontario, Canada). The nowcasting models were developed in combination with readily available real-time environmental and hydro-meteorological data during the bathing season (June-August) of 2008 to 2012. The results of the developed ANN models were compared with historic data and found that the predictions of E. coli concentrations generated by ANN models slightly outperforms than currently used persistence model with better accuracy. The best performing ANN models for each beach are able to predict approximately 74% to 82% of the E. coli concentrations.

scholarly journals Artificial neural network based nowcasting model for beach water quality

2021 ◽  
Author(s):  
Jainy Mavani
Keyword(s):  
Real Time ◽  
Meteorological Data ◽  
Point Sources ◽  
Indicator Organisms ◽  
E Coli ◽  
Water Users ◽  
Ann Models ◽  
Artificial Neural ◽  
Persistence Model ◽  
Beach Water Quality

Recreational water users may be exposed to elevated pathogen levels that originate from various point and non-point sources. Current daily notifications practice depends on microbial analysis of indicator organisms such as Escherichia coli (E. coli) that require 18-24 hours to provide sufficient response. This research evaluated the use of Artificial Neural Networks (ANNs) for real time prediction of E. coli concentration in water at Toronto beaches (Ontario, Canada). The nowcasting models were developed in combination with readily available real-time environmental and hydro-meteorological data during the bathing season (June-August) of 2008 to 2012. The results of the developed ANN models were compared with historic data and found that the predictions of E. coli concentrations generated by ANN models slightly outperforms than currently used persistence model with better accuracy. The best performing ANN models for each beach are able to predict approximately 74% to 82% of the E. coli concentrations.

scholarly journals Waterborne illness and injury: a feasibility study of a site-specific predictive model for beach water quality at Beachway Park in the City of Burlington

2021 ◽  
Author(s):  
Ramien Sereshk
Keyword(s):  
Water Quality ◽  
Predictive Model ◽  
Real Time ◽  
Site Specific ◽  
E Coli ◽  
Time Basis ◽  
A Site ◽  
Persistence Model ◽  
Beach Water Quality ◽  
Beach Water

It is commonly assumed that the persistence model, using day-old monitoring results, will provide accurate estimates of real-time bacteriological concentrations in beach water. However, the persistence model frequently provides incorrect results. This study: 1. develops a site-specific predictive model, based on factors significantly influencing water quality at Beachway Park; 2. determines the feasibility of the site-specific predictive model for use in accurately predicting near real-time E. coli levels. A site-specific predictive model, developed for Beachway Park, was evaluated and the results were compared to the persistence model. This critical performance evaluation helped to identify the inherent inaccuracy of the persistence model for Beachway Park, which renders it an unacceptable approach for safeguarding public health from recreational water-borne illnesses. The persistence model, supplemented with a site-specific predictive model, is recommended as a feasible method to accurately predict bacterial levels in water on a near real-time basis.

scholarly journals Waterborne illness and injury: a feasibility study of a site-specific predictive model for beach water quality at Beachway Park in the City of Burlington

2021 ◽  
Author(s):  
Ramien Sereshk
Keyword(s):  
Water Quality ◽  
Predictive Model ◽  
Real Time ◽  
Site Specific ◽  
E Coli ◽  
Time Basis ◽  
A Site ◽  
Persistence Model ◽  
Beach Water Quality ◽  
Beach Water

It is commonly assumed that the persistence model, using day-old monitoring results, will provide accurate estimates of real-time bacteriological concentrations in beach water. However, the persistence model frequently provides incorrect results. This study: 1. develops a site-specific predictive model, based on factors significantly influencing water quality at Beachway Park; 2. determines the feasibility of the site-specific predictive model for use in accurately predicting near real-time E. coli levels. A site-specific predictive model, developed for Beachway Park, was evaluated and the results were compared to the persistence model. This critical performance evaluation helped to identify the inherent inaccuracy of the persistence model for Beachway Park, which renders it an unacceptable approach for safeguarding public health from recreational water-borne illnesses. The persistence model, supplemented with a site-specific predictive model, is recommended as a feasible method to accurately predict bacterial levels in water on a near real-time basis.

scholarly journals Artificial Neural Network-Based Method for Real-Time Estimation of Compaction Quality of Hot Asphalt Mixes

2021 ◽  
Vol 11 (15) ◽  
pp. 7136
Author(s):  
Zhichao Xue ◽  
Weidong Cao ◽  
Shutang Liu ◽  
Fei Ren ◽  
Qilun Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Field Experiment ◽  
Real Time ◽  
Layer Structure ◽  
Ann Models ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Ann Classifier

With the advancement of intelligent compaction technology, real-time quality control has been widely investigated on the subgrade, while it is insufficient on asphalt pavement. This paper aims to estimate the real-time compaction quality of hot mix asphalt (HMA) using an artificial neural network (ANN) classifier. A field experiment of HMA compaction was designed. The vibration patterns of the drum were identified by using the ANN classifier and classified based on the compaction levels. The vibration signals were collected and the degree of compaction was measured in the field experiment. The collected signals were processed and the features of vibration patterns were extracted. The processed signals were tagged with their corresponding compaction level to form the sample dataset to train the ANN models. Four ANN models with different hidden layer setups were considered to investigate the effect of hidden layer structure on performance. To test the performance of the ANN classifier, the predictions made by ANN were compared with the measuring results from a non-nuclear density gauge (NNDG). The testing results show that the ANN classifier has good performance and huge potential for estimating the compaction quality of HMA in real-time.

scholarly journals Performance evaluation of artificial neural networks in estimating reference evapotranspiration with minimal meteorological data

2013 ◽  
Vol 13 (1) ◽  
pp. 18-27
Keyword(s):  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Learning Rule ◽  
Food And Agriculture ◽  
Air Temperatures ◽  
Food And Agriculture Organization ◽  
Cascade Correlation ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Detailed meteorological data required for the equation of FAO-56 Penman-Monteith (P-M) method that was adopted by Food and Agriculture Organization (FAO) as a standard method in estimating reference evapotranspiration (ETo) are not often available, especially in developing countries. The Hargreaves equation (HG) has been successfully used in some locations to estimate ETo where sufficient data were not available to use the P-M method. This paper investigates the potential of two Artificial Neural Network (ANN) architectures, the multilayer perceptron architecture, in which a backpropagation algorithm (BPANN) is used, and the cascade correlation architecture (CCANN), in which Kalman’s learning rule is embedded in modeling the daily ETo with minimal meteorological data. An overview of the features of ANNs and traditional methods such as P-M and HG is presented, and the advantages and limitations of each method are discussed. Daily meteorological data from three automatic weather stations located in Greece were used to optimize and test the different models. The exponent value of the HG equation was locally optimized, and an adjusted HGadj equation was used. The comparisons were based on error statistical techniques using P-M daily ETo values as reference. According to the results obtained, it was found that taking into account only the mean, maximum and minimum air temperatures, the selected ANN models markedly improved the daily ETo estimates and provided unbiased predictions and systematically better accuracy compared with the HGadj equation. The results also show that the CCANN model performed better than the BPANN model at all stations.

scholarly journals Prediction of Hourly Solar Radiation in Amman-Jordan by Using Artificial Neural Networks

2017 ◽  
Vol 14 (2) ◽  
Keyword(s):  
Solar Radiation ◽  
Meteorological Data ◽  
Coefficient Of Determination ◽  
Narx Models ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Nonlinear Autoregressive ◽  
Yearly Data

In this study, three Artificial Neural Network (ANN) models (Feedforward network, Elman, and Nonlinear Autoregressive Exogenous (NARX)) were used to predict hourly solar radiation in Amman, Jordan. The three models were constructed and tested by using MATLAB software. Meteorological data for the years from 2000 to 2010 were used to train the ANN while the yearly data of 2011 was used to test it. It was found that ANN technique may be used to estimate the hourly solar radiation with an excellent accuracy, and the coefficient of determination of Elman, feedforward and NARX models were found to be 0.97353, 0.97376, and 0.99017, respectively. The obtained results showed that NARX model has the best ability to predict the required solar data, while Elman and feedforward models have the lowest ability to predict it.

DEVELOPMENT OF MULTIPLEX REAL-TIME PCR ASSAY FOR SIX PATHOGENS: RAPID TOOL FOR DIAGNOSIS OF BACTERIAL MENINGITIS

2019 ◽  
pp. 60-66
Author(s):  
Viet Quynh Tram Ngo ◽  
Thi Ti Na Nguyen ◽  
Hoang Bach Nguyen ◽  
Thi Tuyet Ngoc Tran ◽  
Thi Nam Lien Nguyen ◽  
...  
Keyword(s):  
Bacterial Meningitis ◽  
Real Time ◽  
Real Time Pcr ◽  
Diagnostic Methods ◽  
Type B ◽  
E Coli ◽  
Pcr Assays ◽  
Set Up ◽  
Etiological Agents ◽  
Suis Serotype

Introduction: Bacterial meningitis is an acute central nervous infection with high mortality or permanent neurological sequelae if remained undiagnosed. However, traditional diagnostic methods for bacterial meningitis pose challenge in prompt and precise identification of causative agents. Aims: The present study will therefore aim to set up in-house PCR assays for diagnosis of six pathogens causing the disease including H. influenzae type b, S. pneumoniae, N. meningitidis, S. suis serotype 2, E. coli and S. aureus. Methods: inhouse PCR assays for detecting six above-mentioned bacteria were optimized after specific pairs of primers and probes collected from the reliable literature resources and then were performed for cerebrospinal fluid (CSF) samples from patients with suspected meningitis in Hue Hospitals. Results: The set of four PCR assays was developed including a multiplex real-time PCR for S. suis serotype 2, H. influenzae type b and N. meningitides; three monoplex real-time PCRs for E. coli, S. aureus and S. pneumoniae. Application of the in-house PCRs for 116 CSF samples, the results indicated that 48 (39.7%) cases were positive with S. suis serotype 2; one case was positive with H. influenzae type b; 4 cases were positive with E. coli; pneumococcal meningitis were 19 (16.4%) cases, meningitis with S. aureus and N. meningitidis were not observed in any CSF samples in this study. Conclusion: our in-house real-time PCR assays are rapid, sensitive and specific tools for routine diagnosis to detect six mentioned above meningitis etiological agents. Key words: Bacterial meningitis, etiological agents, multiplex real-time PCR

Ozone Concentration Prediction using Artificial Neural Networks

2017 ◽  
Vol 68 (10) ◽  
pp. 2224-2227 ◽  
Author(s):  
Camelia Gavrila
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ozone Concentration ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Time Data ◽  
Marquardt Algorithm ◽  
Sensitivity Tests ◽  
Artificial Neural ◽  
The Relationship

The aim of this paper is to determine a mathematical model which establishes the relationship between ozone levels together with other meteorological data and air quality. The model is valid for any season and for any area and is based on real-time data measured in Bucharest and its surroundings. This study is based on research using artificial neural networks to model nonlinear relationships between the concentration of immission of ozone and the meteorological factors: relative humidity (RH), global solar radiation (SR), air temperature (TEMP). The ozone concentration depends on following primary pollutants: nitrogen oxides (NO, NO2), carbon monoxide (CO). To achieve this, the Levenberg-Marquardt algorithm was implemented in Scilab, a numerical computation software. Performed sensitivity tests proved the robustness of the model and its applicability in predicting the ozone on short-term.

Campylobacter in waterborne epidemics in Finland

2004 ◽  
Vol 4 (2) ◽  
pp. 39-45 ◽  
Author(s):  
M.-L. Hänninen ◽  
R. Kärenlampi
Keyword(s):  
Campylobacter Jejuni ◽  
Causative Agent ◽  
Water Samples ◽  
Fecal Coliforms ◽  
Risk Level ◽  
Source Water ◽  
Indicator Organisms ◽  
Faecal Contamination ◽  
E Coli ◽  
July August September

The sources for drinking water in Finland are surface water, groundwater or artificially recharged groundwater. There are approximately 1400 groundwater plants in Finland that are microbiologically at a high risk level because in most cases they do not use any disinfection treatment. Campylobacter jejuni has caused waterborne epidemics in several countries. Since the middle of the 1980s, C. jejuni has been identified as the causative agent in several waterborne outbreaks in Finland. Between 1998 and 2001, C. jejuni or C. upsaliensis caused seven reported waterborne epidemics. In these epidemics approximately 4000 people acquired the illness. Most of the outbreaks occurred in July, August , September or October. In four of them source water and net water samples were analysed for total coliforms or fecal coliforms, E. coli and campylobacters. We showed that large volumes of water samples in studies of indicator organisms (up to 5000 ml) and campylobacters (4000–20,000 ml) increased the possibility to identify faecal contamination and to detect the causative agent from suspected sources.

Comparison of decay rates of faecal indicator organisms in recreational coastal water and sediment

2002 ◽  
Vol 2 (3) ◽  
pp. 131-138 ◽  
Author(s):  
D.L. Craig ◽  
H.J. Fallowfield ◽  
N.J. Cromar
Keyword(s):  
Water Column ◽  
Coastal Water ◽  
Decay Rates ◽  
Organic Carbon Content ◽  
Indicator Organisms ◽  
Filtration Method ◽  
E Coli ◽  
Water And Sediment ◽  
Increased Risk ◽  
High Organic Carbon Content

A laboratory based microcosm study utilising intact non-sterile sediment cores was undertaken to determine the survival of the faecal indicator organisms Escherichia coli, Enterococcus faecium and somatic coliphage in both recreational coastal water and sediment. Overlying water was inoculated with the test organisms and incubated at 10°C, 20°C or 30°C. E. coli, enterococcus and coliphage were enumerated from the water column and sediment by the membrane filtration method, Enterolert (IDEXX Laboratories) and the double-agar overlay methods respectively on days 0, 1, 2, 7, 14 and 28 following inoculation. It was demonstrated that for all organisms, greater decay (k; d-1) occurred in the water column compared to sediment. Sediment characteristics were found to influence decay, with lowest decay rates observed in sediment consisting of high organic carbon content and small particle size. Decay of E. coli was significantly greater in both the water column and sediment compared with enterococcus and coliphage under all conditions. Decay of enterococcus was found to closely resemble that of coliphage decay. Survival of all organisms was inversely related to temperature, with greatest decay at 30°C. However, increased temperature had a less significant impact on survival of enterococcus and coliphage compared with E. coli. The importance of this study for estimating risk from recreational exposure is great if some pathogenic microorganisms behave similarly to the organisms tested in this study. In particular if survival rates of pathogens are similar to enterococcus and coliphage, then their ability to accumulate in coastal sediment may lead to an increased risk of exposure if these organisms are resuspended into the water column due to natural turbulence or human recreational activity.

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