Neural Network Model for Prediction of Ground Water Level in Metropolitan Considering Rainfall-Runoff as a Parameter
Sanjeev Kumar1, Ajay Indian2, Zubair Khan3
1Sanjeev Kumar, M.Tech (Research Scholar), Department of Computer Science and Engineering, Invertis University, Bareilly, India.
2Ajay Indian, Department of Computer Application, Invertis University, Bareilly, India.
3Zubair Khan, Department of Computer Science and Engineering, Invertis University, Bareilly, India.
Manuscript received on June 05, 2013. | Revised Manuscript received on June 29, 2013. | Manuscript published on July 05, 2013. | PP: 195-198 | Volume-3 Issue-3, July 2013. | Retrieval Number: C1706073313/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In metropolitan area the ground water is the important resource of drinking water. To preserve the ground water level several rain water harvesting techniques are implemented now a days. A neural network model has been developed for ground water level prediction. Various models developed before for ground water level prediction with artificial neural network methodology. Most of these models these models consider rainfall and current ground water level as input parameter. This model considers rainfall-runoff as an important factor which represents the performance of rain water harvesting techniques in urban area. So this model predicts the ground water level with the effect of rain water harvesting techniques.
Keywords: Artificial neural network, ground water level, rainfall-runoff, backpropogation feed forward network, Levenberg-marquardt algorithms.