Forecasting Water Quality Index in Groundwater Using Artificial Neural Network

Monika Kulisz, Justyna Kujawska, Bartosz Przysucha and Wojciech Cel
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Monika Kulisz: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Justyna Kujawska: Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland
Bartosz Przysucha: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Wojciech Cel: Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland

Energies, 2021, vol. 14, issue 18, 1-17

Abstract: 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 R 2 = 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.

Keywords: artificial neural network; prediction; regressions; groundwater; water quality index (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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