Groundwater quality analysis and water quality index prediction by means ‎of machine learning methods‎

  • Authors

    • B. Vamsi JNTUK
    • B. Chandra Sekhar RGUKT
    • V. Vakdevi JNTUK
    • M. Rohit JNTUK
    • Md. Ibrahim Department of Computer Science and Engineering, Lingayas Institute of Management and Technology
    • P. Uday Kiran JNTUK
    • Ch. Srinivas JNTUK
    • M. Gopi Kiran JNTUK https://orcid.org/0000-0002-5972-1094

    Received date: January 2, 2025

    Accepted date: January 27, 2025

    Published date: January 31, 2025

    https://doi.org/10.14419/wkcxnn03
  • Forecasting; Water Parameters; Algorithm; Groundwater Quality; Machine Learning.

  • Abstract

    The study of variations in the quality of groundwater due to subsequent changes in society is a ‎matter of unease as groundwater is regarded as a vital water supply source among all the water ‎sources available. Water quality assessment via monitoring paves the source for arbitrating the ‎appropriateness of quality of water for various purposes including management of water quality. ‎This study was mainly focused on monitoring and assessing the quality of groundwater collected ‎from Madalvarigudem and Mudirajupalem, both from Krishna district, Andhra Pradesh, India. ‎Standard methods were used for analyzing the collected samples for five parameters that include ‎alkalinity, acidity, pH, total hardness (TH) and total dissolved solids (TDS). The groundwater ‎quality parameter analysis indicate high values of pH and TDS, especially in the Mudirajupalem ‎samples as it is located near to agricultural fields. Further, the water quality index (WQI) values ‎supported that groundwater collected from Mudirajupalem is unfit for drinking purpose. This ‎study employed random forest, decision tree and gradient boosted tree techniques for predicting ‎the WQI values and, a hierarchical reconciliation algorithm proved favorable in foretelling water ‎quality parameters. The predicted WQI values meticulously complemented with the obtained ‎experimental results, further endorsing that Mudirajupalem groundwater is not apt for public ‎consumption. Further, this study proposes implementing simple rainwater harvesting systems, which ‎would eventually result in improving the groundwater recharge and maintain groundwater balance ‎so as to making it as a viable source of life‎.

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  • How to Cite

    Vamsi, B., Chandra Sekhar, B., Vakdevi , V., Rohit, M., Ibrahim, M., Uday Kiran, P., Srinivas, C., & Gopi Kiran, M. (2025). Groundwater quality analysis and water quality index prediction by means ‎of machine learning methods‎. SPC Journal of Environmental Sciences, 7(1), 1-8. https://doi.org/10.14419/wkcxnn03

    Received date: January 2, 2025

    Accepted date: January 27, 2025

    Published date: January 31, 2025