Next Article in Journal
Adsorption of Reactive Dye onto Uçá Crab Shell (Ucides cordatus): Scale-Up and Comparative Studies
Next Article in Special Issue
Impacts of OFDI on Host Country Energy Consumption and Home Country Energy Efficiency Based on a Belt and Road Perspective
Previous Article in Journal
A Two-Stage Short-Term Load Forecasting Method Using Long Short-Term Memory and Multilayer Perceptron
Previous Article in Special Issue
Non-Parametric Computational Measures for the Analysis of Resource Productivity
Article

Forecasting Water Quality Index in Groundwater Using Artificial Neural Network

1
Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
2
Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland
*
Author to whom correspondence should be addressed.
Academic Editor: Dino Musmarra
Energies 2021, 14(18), 5875; https://doi.org/10.3390/en14185875
Received: 14 July 2021 / Revised: 1 September 2021 / Accepted: 13 September 2021 / Published: 16 September 2021
Groundwater quality monitoring in the vicinity of drilling sites is crucial for the protection of water resources. Selected physicochemical parameters of waters were marked in the study. The water was collected from 19 wells located close to a shale gas extraction site. The water quality index was determined from the obtained parameters. A secondary objective of the study was to test the capacity of the artificial neural network (ANN) methods to model the water quality index in groundwater. The number of ANN input parameters was optimized and limited to seven, which was derived using a multiple regression model. Subsequently, using the stepwise regression method, models with ever fewer variables were tested. The best parameters were obtained for a network with five input neurons (electrical conductivity, pH as well as calcium, magnesium and sodium ions), in addition to five neurons in the hidden layer. The results showed that the use of the parameters is a convenient approach to modeling water quality index with satisfactory and appropriate accuracy. Artificial neural network methods exhibited the capacity to predict water quality index at the desirable level of accuracy (RMSE = 0.651258, R = 0.9992 and R2 = 0.9984). Neural network models can thus be used to directly predict the quality of groundwater, particularly in industrial areas. This proposed method, using advanced artificial intelligence, can aid in water treatment and management. The novelty of these studies is the use of the ANN network to forecast WQI groundwater in an area in eastern Poland that was not previously studied—in Lublin. View Full-Text
Keywords: artificial neural network; prediction; regressions; groundwater; water quality index artificial neural network; prediction; regressions; groundwater; water quality index
Show Figures

Figure 1

MDPI and ACS Style

Kulisz, M.; Kujawska, J.; Przysucha, B.; Cel, W. Forecasting Water Quality Index in Groundwater Using Artificial Neural Network. Energies 2021, 14, 5875. https://doi.org/10.3390/en14185875

AMA Style

Kulisz M, Kujawska J, Przysucha B, Cel W. Forecasting Water Quality Index in Groundwater Using Artificial Neural Network. Energies. 2021; 14(18):5875. https://doi.org/10.3390/en14185875

Chicago/Turabian Style

Kulisz, Monika, Justyna Kujawska, Bartosz Przysucha, and Wojciech Cel. 2021. "Forecasting Water Quality Index in Groundwater Using Artificial Neural Network" Energies 14, no. 18: 5875. https://doi.org/10.3390/en14185875

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop