groundwater level fluctuation, estimating, artificial neural network, backpropagation algorithms, radial basis function, MATLAB
This paper examines and compares the capability of an artificial neural network (ANN) with five different backpropagation (BP) algorithms, namely Gradient descent with momentum (GDM), Gradient descent with adaptive learning rate and momentum (GDX), The Fletcher-Reeves Conjugate gradient (CGF), Quasi-Newton (BGF) and Levenberg-Marquardt (LM), and a radial basis function (RBF) architecture for estimating groundwater level fluctuation (GLF). MATLAB was used to develop the ANN programming. Five-daily measurements of GLF in an observation well provided the data for analyzing the model. An input model using six time lags to estimate actual GLF and 10 hidden nodes gave an optimum result. In general, the work showed that an ANN could be used to estimate GLF even with relatively few data samples. The Levenberg- Marquardt (LM) algorithm was not only the best algorithm in the BP class but also delivered better results than RBF. This result may be very useful in helping developing countries develop groundwater monitoring and management systems. Such countries typically have very few observation wells and lack long-period time-series data due to budget limitations and government policy.
BYU ScholarsArchive Citation
"Application of an artificial neural network to estimate groundwater level fluctuation,"
Journal of Spatial Hydrology: Vol. 7
, Article 1.
Available at: https://scholarsarchive.byu.edu/josh/vol7/iss2/1