Forecasting of groundwater level in hard rock region using artificial neural network

In hardrock terrain where seasonal streams are not perennial source of freshwater, increase in ground water exploitation has already resulted here in declining ground water levels and deteriorating its’ quality. The aquifer system has shown signs of depletion and quality contamination. Thus, to secure water for the future, water resource estimation and management has urgently become the need of the hour. In order to manage groundwater resources, it is vital to have a tool to predict the aquifer response for a given stress (abstraction and recharge). Artificial neural network (ANN) has surfaced as a proven and potential methodology to forecast the groundwater levels. In this paper, Feed-Forward Network based ANN model is used as a method to predict the groundwater levels. The models are trained with the inputs collected from field and then used as prediction tool for various scenarios of stress on aquifer. Such predictions help in developing better strategies for sustainable development of groundwater resources.

[1]  Witold F. Krajewski,et al.  Rainfall forecasting in space and time using a neural network , 1992 .

[2]  P. C. Nayak,et al.  Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach , 2006 .

[3]  Holger R. Maier,et al.  Understanding the behaviour and optimising the performance of back-propagation neural networks: an empirical study , 1998 .

[4]  Ferenc Szidarovszky,et al.  A neural network model for predicting aquifer water level elevations , 2005, Ground water.

[5]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[6]  P. Gill,et al.  Algorithms for the Solution of the Nonlinear Least-Squares Problem , 1978 .

[7]  Paulin Coulibaly,et al.  Groundwater level forecasting using artificial neural networks , 2005 .

[8]  A Tikhonov,et al.  Solution of Incorrectly Formulated Problems and the Regularization Method , 1963 .

[9]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[10]  Kurt Hornik,et al.  FEED FORWARD NETWORKS ARE UNIVERSAL APPROXIMATORS , 1989 .

[11]  Kenneth Levenberg.  A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[12]  Kurt Hornik,et al.  Neural networks and principal component analysis: Learning from examples without local minima , 1989, Neural Networks.

[13]  Bernard Bobée,et al.  Daily reservoir inflow forecasting using artificial neural networks with stopped training approach , 2000 .

[14]  S. Lallahem,et al.  On the use of neural networks to evaluate groundwater levels in fractured media , 2005 .

[15]  A. Prochazka.  Neural networks and seasonal time-series prediction , 1997 .

[16]  N. Mondal,et al.  Deciphering potential groundwater zone in hard rock through the application of GIS , 2008 .

[17]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[18]  B. Bobée,et al.  Multivariate Reservoir Inflow Forecasting Using Temporal Neural Networks , 2001 .

[19]  M. Sophocleous.  Groundwater recharge and sustainability in the High Plains aquifer in Kansas, USA , 2005 .

[20]  K. Voudouris.  Groundwater Balance and Safe Yield of the coastal aquifer system in NEastern Korinthia, Greece , 2006 .

[21]  R. S. Govindaraju,et al.  Artificial Neural Networks in Hydrology , 2010 .