Prediction of Groundwater Levels Using Different Artificial Neural Network Architectures and Algorithms

Performance of four types of functionally different artificial neural network (ANN) models, namely Feed forward neural network, Elman type recurrent neural network, Input delay neural network and Radial basis function network and fourteen types of algorithms, namely Batch gradient descent (traingd), Batch gradient descent with momentum (traingdm), Adaptive learning rate (traingda), Adaptive learning rate with momentum (traingdx), Resilient backpropagation (trainrp), Fletcher-Reeves update (traincgf), Polak-Ribiere update (traincgp), Powell-Beale restarts (traincgb), Scaled conjugate gradient (trainscg), BFGS algorithm (trainbfg), One step secant algorithm (trainoss), Levenberg-Marquardt (trainlm), Automated regularization (trainbr) and Random order incremental training (trainr) were examined in order to identify an efficient ANN architecture and algorithm that can simulate the water table fluctuations using a relatively short length of groundwater level records. Tirupati, located in Chittoor district of the drought-prone Rayalaseema region in India, having resident population of over 3.0 lakhs and pilgrims of over 50,000 per day was chosen as the study area. As its groundwater levels showed a rapid decline in the last decade due to the overexploitation for the domestic, agricultural and industrial needs, accurate prediction is very essential to plan better conservation of groundwater resources. Results showed that Feed forward neural network trained with training algorithm Levenberg-Marquardt is suitable for accurate prediction of groundwater levels.