Artificial Resources available in punjab
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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.
Introduction
Groundwater always has been as one important and reliable resource to supply drinking and agriculture water and considered to be a reliable resource for supplying consumption needs of different users [1]. Groundwater Reservoir also called ‘aquifer’ is a complicated system and is exposed to either natural or artificial stresses on the aquifer in different chronological levels resulting in the fluctuations of groundwater level. Thus, to exploit and manage groundwater, mathematical models are needed to predict groundwater level fluctuations. Conceptual and physically-based models are considered to be the main tools for depicting hydrological variables and understanding the physical processes taking place in a system [2] but they do have practical limitations. When data are not sufficient, getting accurate predictions is more important than conceiving the actual physics. Empirical models remain a good alternative method and generally provide useful results without a costly calibration time [3]. Artificial Neural Network (ANN) models are such ‘black box’ models with particular properties which are greatly suited to dynamic nonlinear system modeling [4]. ANN has proven to be an extremely useful method for modeling and forecasting of hydrological variables/processes [5-8].
Coppola et al. [9] showed that ANN has potential in accurately predicting of groundwater level fluctuations in an unsteady state of an aquifer influenced by pump and different weather conditions. They noted that predicted results of ANN are more accurate than quantitative models and also showed that ANN models are good at simulating karstic and leaky aquifers where other numerical models are weak in such cases.
In another study by Taiyuan et al. [10] the effects of hydrological, weather and humidity conditions on groundwater level were simulated by neural networks in lower part of Shenyang river basin, North West of china. The used ANN model was able to predict groundwater level with the average error of 0.37 m or lower with the high accuracy. Nadiri [11] had dealt with evaluating of artificial neural network (FFN-LM) ability in modeling of complex aquifer of Tabriz.
In this paper, an attempt has been made to identify the most stable and efficient neural network configuration for predicting groundwater level in the Amritsar and Gurdaspur districts of Punjab. The main purpose of this article is to use artificial neural networks especially feed forward back propagation neural networks to simulate and predict groundwater level. Amritsar and Gurdaspur districts of Punjab were chosen as the study area as the groundwater resources have been overexploited in Punjab including Amritsar and Gurdaspur districts during the last two decades. The groundwater level and quality in Punjab have been decreasing steadily as discussed in numerous studies carried out in different parts of Punjab and Indo-Gangetic basin by various researchers [12-34]. For the planning and management of groundwater resources in the region timely and accurate enough forecasts of ground water levels are required. Therefore, ANN technique was used for simulating the groundwater levels in Amritsar and Gurdaspur districts based on groundwater level data of 4 observation wells in 4 blocks namely Ajnala, Majitha, Rayya and Tarsika of Amritsar and 8 observation wells in 8 blocks namely Batala, Dera Baba Nanak, Dina Nagar, Gurdaspur, Fatehgarh Chrian, Kahmuwan, Kalanaur and Sri Hargovindpur taken from the Punjab Water Resources and Environment Directoate, Chandigarh.
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steel could be an example of resorce
May be