Please use this identifier to cite or link to this item: http://117.252.14.250:8080/jspui/handle/123456789/3072
Title: Application of Artificial Neural Network for Groundwater Level Simulation in Amritsar and Gurdaspur Districts of Punjab, India
Authors: Lohani, A. K.
Krishan, Gopal
Keywords: Artificial neural networks
Groundwater level forecasting
Amritsar; Gurdaspur; Punjab-Aquifer exploitation
Issue Date: 2015
Publisher: OMICS International
Citation: Journal of Earth Science Climate Change 2015, 6:4
Abstract: In this paper, the most stable and efficient neural network configuration for predicting groundwater level in Amritsar and Gurdaspur districts of Punjab, India is identified. For predicting the model efficiency and accuracy, different types of network architectures and training algorithms are investigated and compared. It has been found that accurate predictions can be achieved with a standard feed forward neural network trained with the Levenberg–Marquardt algorithm providing the best results. Good estimation of groundwater level can be achieved by dividing the boreholes/observation wells into different groups of data and designing distinct networks which is validated by the ANN technique and the degree of accuracy of the ANN model in groundwater level forecasting is within acceptable limits. The ANN method has been found to forecast groundwater level in Amritsar and Gurdaspur districts of Punjab, India.
URI: http://117.252.14.250:8080/jspui/handle/123456789/3072
Appears in Collections:Research papers in International Journals

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