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Artificial neural network modeling for groundwater level forecasting in a river island of eastern India
Published in
2010
Volume: 24
   
Issue: 9
Pages: 1845 - 1865
Abstract
Forecasting of groundwater levels is very useful for planning integrated management of groundwater and surface water resources in a basin. In the present study, artificial neural network models have been developed for groundwater level forecasting in a river island of tropical humid region, eastern India. ANN modeling was carried out to predict groundwater levels 1 week ahead at 18 sites over the study area. The inputs to the ANN models consisted of weekly rainfall, pan evaporation, river stage, water level in the drain, pumping rate and groundwater level in the previous week, which led to 40 input nodes and 18 output nodes. Three different ANN training algorithms, viz., gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm, Levenberg-Marquardt (LM) algorithm and Bayesian regularization (BR) algorithm were employed and their performance was evaluated. As the neural network became very large with 40 input nodes and 18 output nodes, the LM and BR algorithms took too much time to complete a single iteration. Consequently, the study area was divided into three clusters and the performance evaluation of the three ANN training algorithms was done separately for all the clusters. The performance of all the three ANN training algorithms in predicting groundwater levels over the study area was found to be almost equally good. However, the performance of the BR algorithm was found slightly superior to that of the GDX and LM algorithms. The ANN model trained with BR algorithm was further used for predicting groundwater levels 2, 3 and 4 weeks ahead in the tubewells of one cluster using the same inputs. It was found that though the accuracy of predicted groundwater levels generally decreases with an increase in the lead time, the predicted groundwater levels are reasonable for the larger lead times as well. © 2009 Springer Science+Business Media B.V.
About the journal
JournalWater Resources Management
ISSN09204741
Open AccessNo
Concepts (43)
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    ADAPTIVE LEARNING RATES
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    Artificial neural network
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    ARTIFICIAL NEURAL NETWORK MODELING
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    Artificial neural network models
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    Bayesian regularization
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    BAYESIAN REGULARIZATION ALGORITHMS
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    Eastern india
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    Gradient descent
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    Ground water level
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    GROUNDWATER LEVEL FORECASTING
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    HUMID REGIONS
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    INPUT NODE
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    INTEGRATED MANAGEMENT
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    LAVENBERG-MARQUARDT ALGORITHM
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    LEAD TIME
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    Levenberg-marquardt algorithm
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    LM ALGORITHM
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    Pan evaporation
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    Performance evaluation
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    PUMPING RATE
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    Study areas
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    Training algorithms
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    TUBEWELLS
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    Backpropagation algorithms
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    Bayesian networks
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    Forecasting
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    Groundwater
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    Groundwater resources
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    Lagrange multipliers
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    Learning algorithms
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    Mathematical models
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    Neural networks
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    Planning
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    Rivers
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    SURFACE WATER RESOURCES
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    Water levels
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    Clustering algorithms
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    Back propagation
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    Bayesian analysis
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    Forecasting method
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    Modeling
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    Water level
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    India