Development of Urbanization Index Model using Multi-dimensional Approach

Shah, Kosha A.; Joshi, Geeta S.

doi:10.5958/2249-7315.2015.00010.6

Cited by 1 publication

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“…The Water Quality Index model [25] used in this study is based on the weighted arithmetic mean method. The limits of the parameter (Si) are selected as limits in class 2 of the rating scale.…”

Section: Wqi Calculationmentioning

confidence: 99%

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Application of artificial neural network (ANN) for water quality index (WQI) prediction for the river Warta, Poland

Kulisz

Kujawska

2021

J. Phys.: Conf. Ser.

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The aim of this paper is to present the potential of using neural network modelling for the prediction of the surface water quality index (WQI). An artificial neural network modelling has been performed using the physicochemical parameters (TDS, chloride, TH, nitrate, and manganese) as an input layer to the model, and the WQI as an output layer. The physicochemical parameters have been taken from five measuring stations of the river Warta in the years 2014-2018 via the Chief Inspectorate of Environmental Protection (GIOŚ). The best results of modelling were obtained for networks with 5 neurons in the hidden layer. A high correlation coefficient (general and within subsets) 0.9792, low level of MSE in each subset (training, test, validation), as well as RMSE at a level of 0.624507639 serve as a confirmation. Additionally, the maximum percentage of an error for WQI value did not exceed 4%, which confirms a high level of conformity of real data in comparison to those obtained during prediction. The aforementioned results clearly present that the ANN models are effective for the prediction of the value of the Surface water quality index and may be regarded as adequate for application in simulation by units monitoring condition of the environment.

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Section: Wqi Calculationmentioning

confidence: 99%

“…The limits of the parameter (Si) are selected as limits in class 2 of the rating scale. The weighting factor of each parameter is calculated and shown in Table 1 [25] on the basis of data given in table 2. Water quality for drinking purposes is usually classified into five categories (Table 3).…”

Section: Wqi Calculationmentioning

confidence: 99%

Application of artificial neural network (ANN) for water quality index (WQI) prediction for the river Warta, Poland

Kulisz

Kujawska

2021

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

Development of Urbanization Index Model using Multi-dimensional Approach

Cited by 1 publication

References 0 publications

Application of artificial neural network (ANN) for water quality index (WQI) prediction for the river Warta, Poland

Application of artificial neural network (ANN) for water quality index (WQI) prediction for the river Warta, Poland

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