Daily prediction of total coliform concentrations using artificial neural networks

Choi, Sang Yong; Bae, Hun Kyun

doi:10.1007/s12205-017-0739-y

Cited by 6 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They stated that flow and temperature variables are effective in the amount of chlorine [13]. In another study, they estimated the daily coliform amount using 6 different models in which neural network-based sedimentation and sedimentation-based artificial variables were preferred, and they stated that the models containing precipitation parameters gave successful results in coliform estimation [14]. Another work from the Iznik lake basin from turkey has been to develop fecal pollution model structures with FFNN for cost-effective lake water quality management studies.…”

Section: Introductionmentioning

confidence: 99%

Applicability of radial-based artificial neural networks (RBNN) on coliform calculation: A case of study

ER¹

2022

Sigma J Eng Nat Sci - Sigma Müh Fen Bil Derg

View full text Add to dashboard Cite

Due to the increasing population, urbanization and economic reasons, it is inevitable to use deep-sea discharges. The fact that there is no alternative and less pollution of the environment is the reason for the preference of deep-sea discharges. In this study, it is aimed to estimate the coliform values of the Tekkekoy deep sea discharge system, which is chosen as an application area, by using a radial-based artificial neural network structure. Firstly, samples taken from the field were examined in a laboratory environment. Values obtained as a result of laboratory studies were used as input in Radial basis artificial neural network (RBNN) architecture. It has been determined that the models prepared by using various combinations have correlation values ranging from 91.5% to 97.2%. The best performing models were models prepared using 10 neurons. From these successful results, it was determined that RBNN structures are useful in coliform prediction.

show abstract

Section: Introductionmentioning

confidence: 99%

Applicability of radial-based artificial neural networks (RBNN) on coliform calculation: A case of study

ER¹

2022

Sigma J Eng Nat Sci - Sigma Müh Fen Bil Derg

View full text Add to dashboard Cite

show abstract

“…A wide range of nonlinear system can be interpreted by neural network modeling technique for use in microbial growth [7][8][9][10]. Nonlinearity is essential to the living microbial culture and limits strongly the use of traditional deterministic modeling techniques to represent the growth of microbes as a function of time [11].…”

Section: Introductionmentioning

confidence: 99%

Escherichia coli growth modeling using neural network

Shamsudin

Rahiman

Taib

et al. 2018

J. Fundam and Appl Sci.

View full text Add to dashboard Cite

The assessment of water microbial quality is normally performed by verification of Escherichia coli where the growth is in nonlinearity. NARX is computational tools that have extensive utilization in solving nonlinear time series problems. It is well known technique that has the ability to predict with efficient and good performance. Using NARX, a highly accurate model was developed to predict the growth of Escherichia coli (E. coli) based on pH water parameter. The multiparameter portable sen used to build and train the neural network. The selection of neural network structure for pH and optical density modelling was optimized and also the training and validation were analyzed. The result exhibited that NARX based on pH water parameter with overall regression is 0.99956. The assessment of water microbial quality is normally performed by verification of Escherichia coli where the growth is in nonlinearity. NARX is computational tools that have extensive utilization in solving nonlinear time series problems. It is well known as one of the technique that has the ability to predict with efficient and good performance. Using NARX, a highly accurate model was developed to predict the growth of Escherichia coli (E. coli) based sor and spectrophotometer data were used to build and train the neural network. The selection of neural network structure for pH and optical density modelling was optimized and also the training and validation were modeling was able to predict the growth of E. coli neural network; NARX; prediction; Escherichia coli; pH; optical density. Keywords

show abstract

Modeling and predicting fecal coliform bacteria levels in oyster harvest waters along Louisiana Gulf coast

Wang

Deng

2019

Ecological Indicators

View full text Add to dashboard Cite

Daily prediction of total coliform concentrations using artificial neural networks

Cited by 6 publications

References 20 publications

Applicability of radial-based artificial neural networks (RBNN) on coliform calculation: A case of study

Applicability of radial-based artificial neural networks (RBNN) on coliform calculation: A case of study

Escherichia coli growth modeling using neural network

Modeling and predicting fecal coliform bacteria levels in oyster harvest waters along Louisiana Gulf coast

Contact Info

Product

Resources

About