The application of the Radial Basis Function Neural Network in estimation of nitrate contamination in Manawatu river

Faramarzi, Mahdi; Yunus, Mohd Amri Md; Nor, Alif Syarafi Mohamad; Ibrahim, Suhaimi

doi:10.1109/iccst.2014.7045005

Cited by 3 publications

(2 citation statements)

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“…Results demonstrated the effectiveness of the proposed methods. Researchers tended to divide the training set data into 70% to 90% of the total data [39,42,49,52,72,[120][121][122][123][124][125][126][127]. Iglesias et al [35] divided the data into training (90%) and testing sets (10%).…”

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

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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.

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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

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“…Training a network by BP involved three stages which are the feedforward of the input training patterns, the BP of the associated error and the adjustment of the weights [6][7][8]. ANN is a reliable forecasting method in 125 many applications, nevertheless load identification is a difficult task.…”

Section: Introductionmentioning

confidence: 99%