dc.contributor.author |
Lohani, A. K. |
|
dc.contributor.author |
Krishan, Gopal |
|
dc.date.accessioned |
2019-07-11T10:03:08Z |
|
dc.date.available |
2019-07-11T10:03:08Z |
|
dc.date.issued |
2015 |
|
dc.identifier.citation |
J Geol Geosci 2015, 4:3 |
en_US |
dc.identifier.uri |
http://117.252.14.250:8080/jspui/handle/123456789/3073 |
|
dc.description.abstract |
This paper presents an efficient and stable artificial neural network (ANN) model for predicting groundwater level in
south-east Punjab, India. After improving the model accuracy using different types of network architectures and training
algorithms, it has been observed that best results can be achieved with a standard feed forward neural network trained
with the Levenberg–Marquardt algorithm. Good estimation of groundwater level can be achieved by designing distinct
networks for different sites and ANN method has been found to forecast groundwater level in Faridkot, Ferozepur,
Ludhiana and Patiala districts of Punjab, India with reasonable accuracy |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
SCIAEON |
en_US |
dc.subject |
Artificial neural networks |
en_US |
dc.subject |
Groundwater level forecasting |
en_US |
dc.subject |
South-east Punjab- Aquifer exploitation |
en_US |
dc.title |
Groundwater Level Simulation Using Artificial Neural Network in Southeast, Punjab, India |
en_US |
dc.type |
Article |
en_US |