National Academy of Agricultural Sciences (NAAS)
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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 2018: 95.39 NAAS RATING 2020: 5.38 |
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.
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