Using statistical and artificial neural network models to forecast potentiometric levels at a deep well in South Texas

Reliable forecasts of monthly and quarterly fluctuations in groundwater levels are necessary for short- and medium-term planning and management of aquifers to ensure proper service of seasonal demands within a region. Development of physically based transient mathematical models at this time scale p...

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Bibliographic Details
Published in:Environmental earth sciences 2007-01, Vol.51 (6), p.885-895
Main Author: Uddameri, V
Format: Article
Language:English
Subjects:
ANN
Aquifer management
Aquifers
Back percolation
Coastal aquifers
Correlation coefficient
Fluctuations
Groundwater levels
Mathematical models
Mean square errors
Neural networks
Studies
Systems analysis
Time-series modeling
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Summary:Reliable forecasts of monthly and quarterly fluctuations in groundwater levels are necessary for short- and medium-term planning and management of aquifers to ensure proper service of seasonal demands within a region. Development of physically based transient mathematical models at this time scale poses considerable challenges due to lack of suitable data and other uncertainties. Artificial neural networks (ANN) possess flexible mathematical structures and are capable of mapping highly nonlinear relationships. Feed-forward neural network models were constructed and trained using the back-percolation algorithm to forecast monthly and quarterly time-series water levels at a well that taps into the deeper Evangeline formation of the Gulf Coast aquifer in Victoria, TX. Unlike unconfined formations, no causal relationships exist between water levels and hydro-meteorological variables measured near the vicinity of the well. As such, an endogenous forecasting model using dummy variables to capture short-term seasonal fluctuations and longer-term (decadal) trends was constructed. The root mean square error, mean absolute deviation and correlation coefficient (R) were noted to be 1.40, 0.33 and 0.77 m, respectively, for an evaluation dataset of quarterly measurements and 1.17, 0.46, and 0.88 m for an evaluative monthly dataset not used to train or test the model. These statistics were better for the ANN model than those developed using statistical regression techniques.
ISSN:0943-0105
1866-6280
1432-0495
1866-6299
DOI:10.1007/s00254-006-0452-5