Drought Forecasting, using Artificial Neural Network (ANN) and Predict Values of Drought Condition Derived using Enhanced Vegetation Index (EVI) Data

Authors

  • Rajesh Kumar Sharma A-51 Exotica Villas Airport Road Bhopal (M.P), India
  • Mayank Rajput Director. Alpha College, NH 46 Bairagarh Khuman, Dist Sehore (M.P), India
  • Rahul Sharma A-51 Exotica Villas Airport Road Bhopal (M.P), India

DOI:

https://doi.org/10.26438/ijcse/v11i1.1416

Keywords:

Artificial Neural Network, Enhanced Vegetation Index

Abstract

This paper focuses on drought forecasting, using Artificial Neural Network (ANN) and predicts the values of drought condition derived using Remote Sensing (EVI) data of Indore (M.P). We have used the EVI data as input data of ANN model for drought forecasting, and determine Standard Enhanced Vegetation Index (SEVI). Artificial Neural networks operate on the principle of learning from a training set. There is a large variety of neural network models and learning procedures. Two classes of neural networks that are usually used for prediction applications are feed-forward networks and recurrent networks. They often train both of these networks using back-propagation algorithm.

References

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[2] Crippen, Robert E.: Calculates the vegetation index faster. Remote Sensing of Environment, Vol.34, Issue.1, pp.71–73, 1990.

[3] Marzban, C. and Stumpf, G. J.: A neural network for tornado prediction based on Doppler radar-derived attributes. J. Appl. Meteor., Vol.35, pp.617–626, 1996.

[4] Mu¨ller, B., and Reinhardt, J.: Neural Networks: An Introduction, the Physics of Neural Networks Series, Springer-Verlag, Vol.2, pp.266, 1991.

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Published

2023-01-31
CITATION
DOI: 10.26438/ijcse/v11i1.1416
Published: 2023-01-31

How to Cite

[1]
R. K. Sharma, M. Rajput, and R. Sharma, “Drought Forecasting, using Artificial Neural Network (ANN) and Predict Values of Drought Condition Derived using Enhanced Vegetation Index (EVI) Data”, Int. J. Comp. Sci. Eng., vol. 11, no. 1, pp. 14–16, Jan. 2023.

Issue

Vol. 11 No. 1 (2023): IJCSE January Edition

Section

Research Article

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