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  Authors

Nuthala Mythri,M.Sonia,Kancharla Durgaprasad,Tadi Sai Kiran

  Keywords

drinking water, irrigation water

  Abstract


Water is a vital resource for the survival of humans, animals, and plants. However, its quality is not always suitable for drinking or other uses due to factors like industrialization, pollution, and natural occurrences. The World Health Organization has guidelines to determine the threshold levels of various parameters in water samples. The Water Quality Index (WQI) and Irrigation WQI (IWQI) are used to assess overall water quality. Collecting and measuring water samples can be challenging, but a proposed network architecture uses real-time data collection and machine learning tools to automatically determine the suitability of water samples for drinking and irrigation. The network is based on LoRa and considers the land topology. Simulations indicate a partial mesh network topology is most suitable. Since there is a lack of large datasets on drinking and irrigation water, new datasets were developed for training machine learning models. Three models (Random Forest, Logistic Regression, and Support Vector Machine) were evaluated, with Logistic Regression performing best for drinking water and Support Vector Machine for irrigation water. Recursive feature elimination was used to identify the water parameters with the greatest influence on classification accuracies.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2405566

  Paper ID - 260332

  Author type - Indian Author

  Page Number(s) - f278-f281

  Pubished in - Volume 12 | Issue 5 | May 2024

  DOI (Digital Object Identifier) -   

  No Of Downloads - 18

  Author Country - India, 504302, mancherial, mancherial, 504302, Science and Technology

  Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Published Paper PDF : - http://www.ijcrt.org/papers/IJCRT2405566

  Published Paper URL: : - http://ijcrt.org/viewfull.php?&p_id=IJCRT2405566

  Published Paper PDF Downlaod: - download.php?file=IJCRT2405566

  Cite this article

Nuthala Mythri,M.Sonia,Kancharla Durgaprasad,Tadi Sai Kiran,   "A Tessellation Model for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 5, pp.f278-f281, May 2024, Available at :http://www.ijcrt.org/papers/IJCRT2405566.pdf

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