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Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in Kathajodi-Surua Inter-basin of Odisha, India

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Title Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in Kathajodi-Surua Inter-basin of Odisha, India
Not Available
 
Creator Mohanty, S., Jha, M. K., Kumar, A. and Panda, D. K.
 
Subject Groundwater flow modeling MODFLOW Artificial neural network Deltaic aquifer system Kathajodi-Surua Inter-basin
 
Description Not Available
In view of worldwide concern for the sustainability of groundwater resources, basin-wide modeling of
groundwater flow is essential for the efficient planning and management of groundwater resources in
a groundwater basin. The objective of the present study is to evaluate the performance of finite difference-based
numerical model MODFLOW and the artificial neural network (ANN) model developed in this
study in simulating groundwater levels in an alluvial aquifer system. Calibration of the MODFLOW was
done by using weekly groundwater level data of 2 years and 4 months (February 2004 to May 2006)
and validation of the model was done using 1 year of groundwater level data (June 2006 to May
2007). Calibration of the model was performed by a combination of trial-and-error method and automated
calibration code PEST with a mean RMSE (root mean squared error) value of 0.62 m and a mean
NSE (Nash–Sutcliffe efficiency) value of 0.915. Groundwater levels at 18 observation wells were simulated
for the validation period. Moreover, artificial neural network models were developed to predict
groundwater levels in 18 observation wells in the basin one time step (i.e., week) ahead. The inputs to
the ANN model consisted of weekly rainfall, evaporation, river stage, water level in the drain, pumping
rate of the tubewells and groundwater levels in these wells at the previous time step. The time periods
used in the MODFLOW were also considered for the training and testing of the developed ANN models.
Out of the 174 data sets, 122 data sets were used for training and 52 data sets were used for testing. The
simulated groundwater levels by MODFLOW and ANN model were compared with the observed groundwater
levels. It was found that the ANN model provided better prediction of groundwater levels in the
study area than the numerical model for short time-horizon predictions.
Not Available
 
Date 2018-12-03T10:05:27Z
2018-12-03T10:05:27Z
2013-04-24
 
Type Research Paper
 
Identifier Mohanty, S., Jha, M. K., Kumar, A. and Panda, D. K. 2013. Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in Kathajodi-Surua Inter-basin of Odisha, India. Journal of Hydrology 495:38-51.
0022-1694
http://krishi.icar.gov.in/jspui/handle/123456789/15287
 
Language English
 
Relation Not Available;
 
Publisher Elsevier B.V