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Using an Artificial Neural Network Model to Predict Groundwater Quality in the Southern Region of Jabalpur City

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Abstract

The current research focuses on the chemical evaluation of groundwater in the southern region to the Jabalpur city that is utilized for drinking and is based on the Water Quality Index. To conduct the groundwater quality analysis and subsequently calculate the Water Quality Index, water samples from 37 wells were collected within the research area. Twelve different water characteristics were taken into account, including pH, Total Hardness, Total Dissolved Solids, Calcium, Magnesium, Sodium, Potassium, Iron, Fluoride, Bicarbonate, Sulphate, and Chloride. The distribution of the groundwater samples, as determined by the computed WQI, was found to be 0.00% in the excellent category, 0.00% in the Good category, 86.00% in the Poor category, 14% in the Very Poor category, 0.00% in the unsuitable category, respectively. Using IBM® SPSS® Statistics 16 software, an artificial neural network model (ANNM) was created to forecast changes in the groundwater water quality index (WQI). Sum of squares error functions and a coefficient of determination are used to assess the ANNM for both training and testing samples (R2). The findings demonstrated that the ANNM was highly effective in making predictions, with sum of squares errors for the training and testing samples of 0.048 and 0.033, respectively, and an R2 value of 0.995. It is also shown that the factors HCO3, Fluoride, and Total Hardness have a significant influence on model prediction.

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Data available on request due to privacy/ethical restrictions.

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Acknowledgements

The faculty and students of GGITS Jabalpur conducted the research, and the authors express gratitude to the Institution’s management and authorities for their support in executing the project.

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Authors and Affiliations

  1. Gyan Ganga College of Technology, Jabalpur, Madhya Pradesh, India

    Niranjan M. Damle & Arun Kumar Sharma

  2. Gyan Ganga Institute of Technology and Sciences, Jabalpur, Madhya Pradesh, India

    Sudeepto Mukherjee & Sourabh Sahu

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  1. Niranjan M. Damle
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Correspondence to Niranjan M. Damle.

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M. Damle, N., Mukherjee, S., Sahu, S. et al. Using an Artificial Neural Network Model to Predict Groundwater Quality in the Southern Region of Jabalpur City. Water Air Soil Pollut 234, 701 (2023). https://doi.org/10.1007/s11270-023-06725-7

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