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  Published Paper Details:

  Paper Title

Water Quality Monitoring And Forecasting Using Machine Learning

  Authors

  Bojja Navi Namratta,  Chenna Nikhitha,  Deepak Menon,  Dr. S. Latha

  Keywords

Python, Water quality forecasting, ARIMA model

  Abstract


Water is a crucial resource and has a wide range of applications. Water is essential for the sustainability of life on planet Earth. From bathing to cooking, water is used in almost every activity we perform on a daily basis. Earth is covered by almost 70% water but not all of it is useable and safe for utilization. Only about three percent of Earth's water is freshwater. Of that, only about 1-2 percent can be used as drinking water; the rest is locked up in glaciers, ice caps, and permafrost, or buried deep in the ground. Most of our drinking water comes from rivers and streams. In our constantly developing world, the increased rate of pollution and urbanization have started to contaminate the water supply making it unfit for drinking. Thus, a water quality monitoring system is needed to monitor and forecast its quality, and safeguard human health. To perform this task a system is developed using the popular Python programming language which helps to calculate the quality of water based on the readings taken from various sensors. We use an Auto-Regressive Integrated Moving Average (ARIMA) model to monitor water quality.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2306031

  Paper ID - 238789

  Page Number(s) - a273-a277

  Pubished in - Volume 11 | Issue 6 | June 2023

  DOI (Digital Object Identifier) -   

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

  E-ISSN Number - 2320-2882

  Cite this article

  Bojja Navi Namratta,  Chenna Nikhitha,  Deepak Menon,  Dr. S. Latha,   "Water Quality Monitoring And Forecasting Using Machine Learning", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.11, Issue 6, pp.a273-a277, June 2023, Available at :http://www.ijcrt.org/papers/IJCRT2306031.pdf

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ISSN: 2320-2882
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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