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International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2019)
[Effective from January 1, 2019]
For more details click here

ICV 2017: 100.00
Index Copernicus ICI Journals Master List 2017 - IJCMAS--ICV 2017: 100.00
For more details click here

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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : editorijcmas@gmail.com / submit@ijcmas.com
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2017: 100.00
NAAS RATING 2018: 5.38

Int.J.Curr.Microbiol.App.Sci.2018.7(12): 3358-3367
DOI: https://doi.org/10.20546/ijcmas.2018.712.386


Simulation of Groundwater Level Using Recurrent Neural Network (RNN) in Raichur District, Karnataka
Anandakumar*, B. Maheshwara Babu, G.V. Srinivasa Reddy, U. Satishkumar and Prasad Kulkarni
Department of SWE, College of Agricultural Engineering, UAS, Raichur-584104, Karnataka, India
*Corresponding author
Abstract:

A proper design of the architecture of Artificial Neural Network (ANN) models can provide a robust tool in water resources modelling and forecasting. The performance of different neural networks in groundwater level forecasting was examined in order to identify an optimal ANN model for groundwater level forecast. The Devasugur nala watershed was selected for the study, located at northern part of Raichur district Karnataka and comes under middle Krishna river basin. Elman or Recurrent Neural Network (RNN) trained with Bayesian Regularization (BR), Levenberg Marquardt (LM) and Gradient Descent with Momentum and Adaptive Learning Rate Back propagation (GDX) algorithm models were developed. The results revealed that RNN with LM algorithm provided better prediction than the other models with highest correlation efficiency (0.8311) and lowest RMSE (0.9896) value during validation period. Overall it was observed that the ANN based algorithm was a better choice for the groundwater level forecasting.


Keywords: Artificial neural network, Elman neural network, Recurrentneural network, Groundwater level forecasting
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How to cite this article:

Anandakumar, B. Maheshwara Babu, G.V. Srinivasa Reddy, U. Satishkumar and Prasad Kulkarni. 2018. Simulation of Groundwater Level Using Recurrent Neural Network (RNN) in Raichur District, Karnataka.Int.J.Curr.Microbiol.App.Sci. 7(12): 3358-3367. doi: https://doi.org/10.20546/ijcmas.2018.712.386