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International Journal of Current Microbiology and Applied Sciences (IJCMAS)
IJCMAS is now DOI (CrossRef) registered Research Journal. The DOIs are assigned to all published IJCMAS Articles.
Index Copernicus ICI Journals Master List 2018 - IJCMAS--ICV 2018: 95.39 For more details click here
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National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2020)
[Effective from January 1, 2020]
For more details click here

ICV 2018: 95.39
Index Copernicus ICI Journals Master List 2018 - IJCMAS--ICV 2018: 95.39
For more details click here

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Original Research Articles

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 2018: 95.39
NAAS RATING 2020: 5.38

Int.J.Curr.Microbiol.App.Sci.2018.7(2): 2947-2954
DOI: https://doi.org/10.20546/ijcmas.2018.702.358


Groundwater Level Prediction Using Artificial Neural Network Model
Pallavi Porte1, Rajendra Kumar Isaac1, Kipoo Kiran Singh Mahilang2, Khilendra Sonboier3 and Pankaj Minj3
1Department of WRE, SHIATS, Allahabad, UP, India
2Department of FMP, SVCAET&RS, IGKV, Raipur, India
3Department of APFE, SHIATS, Allahabad, UP, India
*Corresponding author
Abstract:

Forecasting of stream flow and ground water level changes became an important component of water resources system control and challenging task for water resources engineers and managers. The ground water level data and rainfall data of twenty years from 1996 to 2015 were collected. Artificial neural network (ANN) is used to predict water resources variable. The model was trained, validated and tested for randomly divided samples. The regression analysis shows good correlation between each other within the range 0.12 to 0.97 of Abhanpur block. The performance evaluation of ANN model showed highest value of correlation coefficient (R) as 0.9781 during training for the month March/April/May of Abhanpur block. Thus it can be determined that ANN provides a feasible method in predicting groundwater level in Raipur district of Chhattisgarh state.


Keywords: Artificial neural network, Groundwater level, Rainfall, Levenberg
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How to cite this article:

Pallavi Porte, Rajendra Kumar Isaac, Kipoo Kiran Singh Mahilang, Khilendra Sonboier and Pankaj Minj. 2018. Groundwater Level Prediction Using Artificial Neural Network Model.Int.J.Curr.Microbiol.App.Sci. 7(2): 2947-2954. doi: https://doi.org/10.20546/ijcmas.2018.702.358