The improvement of total organic carbon forecasting using neural networks discharge model

Yeon, In-Sung; Jun, Kye-Won; Lee, H.J.

doi:10.1080/09593330802468780

Cited by 13 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Results showed that the predicted values of the ANN model were close to the measured value. Yeon et al [96] combined Precip and Q to realize a one-step prediction of Q. Then, the connected system utilized the prediction value of Q and historical TOC to fulfill the one-step prediction of TOC.…”

Section: Artificial Neural Network Models For Water Quality Predictionmentioning

confidence: 99%

A Review of the Artificial Neural Network Models for Water Quality Prediction

et al. 2020

View full text Add to dashboard Cite

Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.

show abstract

Section: Artificial Neural Network Models For Water Quality Predictionmentioning

confidence: 99%

A Review of the Artificial Neural Network Models for Water Quality Prediction

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Fatou and Julia showed (by means of a perturbation argument) that the number of neutral cycles is at most 4d 4. However, in 1987 Shishikura [31] used quasiconformal surgery techniques to give the sharp bound of at most 2d 2 non-repelling orbits for any rational map of degree d . This bound is commonly called the Fatou-Shishikura inequality.…”

Section: The Mapmentioning

confidence: 99%

“…We writè WD btc in the argument below. In the first step of the argument we will use the Sub-additive Ergodic Theorem [31] for the sub-additive function sequence n .i/ WD log ' t .A i n /:…”

Section: Thermodynamical Formalismmentioning

confidence: 99%

“…Apparently it did not find much use at that time, until it got re-introduced by Young in his book [31] from 1982. After that, Daubechies, Grossmann and Morlet took the key step of connecting frames with wavelets and Gabor systems in the paper [15].…”

Section: A Survey On Frames and Operatorsmentioning

confidence: 99%

“…Constituent concentrations can be used by water-quality managers for comparison of current water-quality conditions to water-quality standards. Examination of stream flow and physical properties of water that act as surrogates for constituents of interest also helps for collection of water-quality samples [18,25,29,31].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fractals, Wavelets, and their Applications

Bandt¹,

Barnsley²,

Devaney³

et al. 2014

Springer Proceedings in Mathematics &Amp; Statistics

View full text Add to dashboard Cite

This book series features volumes composed of select contributions from workshops and conferences in all areas of current research in mathematics and statistics, including OR and optimization. In addition to an overall evaluation of the interest, scientific quality, and timeliness of each proposal at the hands of the publisher, individual contributions are all refereed to the high quality standards of leading journals in the field. Thus, this series provides the research community with well-edited, authoritative reports on developments in the most exciting areas of mathematical and statistical research today.

show abstract