Open Access   Article Go Back

Drought Forecasting, using Artificial Neural Network (ANN) and Predict Values of Drought Condition Derived using NDVI Data

Rajesh Kumar Sharma1 , Mayank Rajput2 , Rahul Sharma3

Section:Research Paper, Product Type: Journal Paper
Volume-8 , Issue-1 , Page no. 191-193, Jan-2020

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v8i1.191193

Online published on Jan 31, 2020

Copyright © Rajesh Kumar Sharma, Mayank Rajput, Rahul Sharma . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Rajesh Kumar Sharma, Mayank Rajput, Rahul Sharma, “Drought Forecasting, using Artificial Neural Network (ANN) and Predict Values of Drought Condition Derived using NDVI Data,” International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.191-193, 2020.

MLA Style Citation: Rajesh Kumar Sharma, Mayank Rajput, Rahul Sharma "Drought Forecasting, using Artificial Neural Network (ANN) and Predict Values of Drought Condition Derived using NDVI Data." International Journal of Computer Sciences and Engineering 8.1 (2020): 191-193.

APA Style Citation: Rajesh Kumar Sharma, Mayank Rajput, Rahul Sharma, (2020). Drought Forecasting, using Artificial Neural Network (ANN) and Predict Values of Drought Condition Derived using NDVI Data. International Journal of Computer Sciences and Engineering, 8(1), 191-193.

BibTex Style Citation:
@article{Sharma_2020,
author = {Rajesh Kumar Sharma, Mayank Rajput, Rahul Sharma},
title = {Drought Forecasting, using Artificial Neural Network (ANN) and Predict Values of Drought Condition Derived using NDVI Data},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2020},
volume = {8},
Issue = {1},
month = {1},
year = {2020},
issn = {2347-2693},
pages = {191-193},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5649},
doi = {https://doi.org/10.26438/ijcse/v8i1.191193}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v8i1.191193}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5649
TI - Drought Forecasting, using Artificial Neural Network (ANN) and Predict Values of Drought Condition Derived using NDVI Data
T2 - International Journal of Computer Sciences and Engineering
AU - Rajesh Kumar Sharma, Mayank Rajput, Rahul Sharma
PY - 2020
DA - 2020/01/31
PB - IJCSE, Indore, INDIA
SP - 191-193
IS - 1
VL - 8
SN - 2347-2693
ER -

VIEWS PDF XML
43 37 downloads 8 downloads
  
  
           

Abstract

This paper focuses on drought forecasting, using Artificial Neural Network (ANN) and predicts the values of drought condition derived using Remote Sensing data of Indore (M.P). We have used the NDVI data as input data of ANN model for drought forecasting, and determine Standard Vegetation Index (SNDVI). 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.

Key-Words / Index Term

Data Source, Artificial Neural Network.

References

[1]. Bankert, R. L.: Cloud classification of AVHRR Imagery in maritime regions using a probabilistic neural network, J. Appl. Meteorol., 33, pp.909 – 918, 1994.
[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, 2, pp.266, 1991.
[5]. Sellers, P. J.: Canopy reflectance, photosynthesis and transpiration, International Journal of Remote Sensing, 6: 8, pp.1335-1372, 1985.