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.
Highlights
For depicting hydrological variables and understanding the physical processes taking place in a hydrological system, conceptual and physically-based models are considered to be the main tools [1]
Research and development in Artificial Neural Networks (ANNs) started with an attempt to model the bio-physiology of the human brain, creating models which would be capable of mimicking processes characteristics of human on a computational level
The aim of the present study is to test the ability of ANN in forecasting groundwater level fluctuations in Fardikot, Ferozepur, Ludhiana and Patiala districts of Punjab
Summary
For depicting hydrological variables and understanding the physical processes taking place in a hydrological system, conceptual and physically-based models are considered to be the main tools [1]. When data are not sufficient, getting accurate predictions is more important than conceiving the actual physics of the system. In such situations, empirical models remain a good alternative method and generally provide useful results without a costly calibration time [2]. Neural networks are widely regarded as a potentially effective approach for handling large amounts of dynamic, non-linear and noisy data, especially in situations where the underlying physical relationships are not fully understood
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