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  Published Paper Details:

  Paper Title

WATER REQUIREMENT FORECASTING FOR CITY SYSTEM USING MACHINE LEARNING

  Authors

  Sushant Kumbhar,  Vandan Jadhav,  Bhagyashree Yelameli,  Sakshi Dhamale,  Prateeksha Chouksey

  Keywords

water supply, supervised learning, linear regression, SVM algorithm, water demand

  Abstract


: Water is essential to the existence of life on Earth. The causes of dehydration are natural and anthropogenic. In the world, the amount of freshwater remains constant for a period of time, but the population has already reached it. So aim for freshwater that is stronger day by day. Proper management and prognosis is required for effective and efficient water use systems. Water demand and forecasting are the mainstays of urban water management. Machine learning is one of the most well-known methods of prediction. Machine learning is a data analysis method that gives a machine the ability to read without being completely organized. Unlike traditional methods of predicting required that were incorrectly structured and poorly structured historical data, machine learning looks or has the power to analyze that data This technique predicts the annual water demand for the succeeding year employing a statistical algorithmic program and water demand for industries, agriculture, domestic and public gardens. This multi?method prediction suggests potential for extension to advanced probabilistic prediction issues in alternative fields.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2204091

  Paper ID - 217767

  Page Number(s) - a745-a748

  Pubished in - Volume 10 | Issue 4 | April 2022

  DOI (Digital Object Identifier) -   

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

  E-ISSN Number - 2320-2882

  Cite this article

  Sushant Kumbhar,  Vandan Jadhav,  Bhagyashree Yelameli,  Sakshi Dhamale,  Prateeksha Chouksey,   "WATER REQUIREMENT FORECASTING FOR CITY SYSTEM USING MACHINE LEARNING", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.10, Issue 4, pp.a745-a748, April 2022, Available at :http://www.ijcrt.org/papers/IJCRT2204091.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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