GROUND WATER LEVEL PREDICTION USING MACHINE LEARNING

Authors

  • Dr. J Rajaram
  • D Navya
  • E Krishna
  • G Harika

Keywords:

Soil Moisture, SVM, ANN, Machine Learning

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.

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Published

2020-12-30

How to Cite

Rajaram, D. J. . ., Navya, D. . ., Krishna, E. . ., & Harika, G. . . (2020). GROUND WATER LEVEL PREDICTION USING MACHINE LEARNING. The Journal of Contemporary Issues in Business and Government, 26(3), 106–112. Retrieved from https://cibgp.com/au/index.php/1323-6903/article/view/525

Issue

Vol. 26 No. 3 (2020): The journal of contemporary issues in business and government

Section

Articles

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