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GROUND WATER LEVEL PREDICTION USING MACHINE LEARNING

    Author

    • T.ABDUL RAHEEM,M. ERAMMA
,

Document Type : Research Article

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Abstract

This Paper introduces the implementation of different supervised learning techniques for producing accurate estimates of ground water, including meteorological and remotely sensed data. The models thus developed can be extended to be used by the personal remote sensing systems developed in the Center for Self-Organizing Intelligent Systems (CSOIS). To analyze these data and to extract relevant features, such as essential climate variables (ECV), specific methodologies need to be exploited.. The new algorithm enhances the temporal resolution of high spatial resolution of soil moisture observations with good quality and can benefit multiple soil moisture-based applications and research

Keywords

  • Soil Moisture
  • SVM
  • ANN
  • Machine Learning
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    • Article View: 98
    • PDF Download: 493
European Journal of Molecular & Clinical Medicine
Volume 4, Issue 1
January 2017
Page 183-189
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  • PDF 500.5 K
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  • Article View: 98
  • PDF Download: 493

APA

ERAMMA, T. R. (2022). GROUND WATER LEVEL PREDICTION USING MACHINE LEARNING. European Journal of Molecular & Clinical Medicine, 4(1), 183-189.

MLA

T.ABDUL RAHEEM,M. ERAMMA. "GROUND WATER LEVEL PREDICTION USING MACHINE LEARNING". European Journal of Molecular & Clinical Medicine, 4, 1, 2022, 183-189.

HARVARD

ERAMMA, T. R. (2022). 'GROUND WATER LEVEL PREDICTION USING MACHINE LEARNING', European Journal of Molecular & Clinical Medicine, 4(1), pp. 183-189.

VANCOUVER

ERAMMA, T. R. GROUND WATER LEVEL PREDICTION USING MACHINE LEARNING. European Journal of Molecular & Clinical Medicine, 2022; 4(1): 183-189.

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For Special Issue Proposal : editor.ejmcm21@gmail.com

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