Abstract
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.
References
Ahmad S, Simonovic SP (2005) An artificial neural network model for generating hydrograph from hydro-meteorological parameters. J Hydrol 315:236–251
Anctil F, Claude M, Charles P, Vazken A (2004) A soil moisture index as an auxillary ANN input for stream flow forecasting. J Hydrol 286:155–167
ASCE (2000a) Artificial neural networks in hydrology: 1: preliminary concepts. J Hydrol Eng 5:115–123
ASCE (2000b) Artificial neural networks in hydrology II. hydrologic applications. J Hydrol Eng 5:124–137
Baker ET (1979) Stratigraphic and hydrogeological framework of part of the coastal plain of Texas. Texas Department of Water Resources, Austin, p 43
Burn DA, Ryan JTA (1983) A diagnostic test for lack of fit in regression models. In: Proceedings of the statistical computing section, American Statistical Association, pp 286–290
Coppola Jr E, Poulton M, Charles E, Dustman J, Szidarozvsky F (2003) Application of artificial neural networks to complex groundwater management problems. Nat Resour Res 12:303–320
Coulibaly P, Anctil F, Aravena R, Bobee B (2001) Artificial neural network modeling of water table depth fluctuations. Water Resour Res 37:885–896
Daliakopoulos IN, Coulibaly P, Tsanis IK (2005) Groundwater level forecasting using artificial neural networks. J Hydrol 309:229–240
Fernando DA, Jayawardena AW (1998) Runoff forecasting using RBF networks with OLS algorithm. J Hydrol Eng 3:203–209
Gujarati D (1979) Basic econometrics. Mc-Graw Hill, New York
Hamilton C (1993) Regression with graphics: a second course in statistics. PWS-Kent Publishers, Boston
Hornik K, Stinchcombe M, White M (1989) Multilayer feed forward networks as universal approximators. Neural Netw 2:359–366
Hsu K-L, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of the rainfall-runoff process. Water Resour Res 31:2517–2530
Jurik M (1990) Back-percolation: assigning local error in feed-forward perceptron networks. Jurik Research and Consulting, Aptos pp 1
Moradkhani H, Hsu K-L, Gupta H, Sorooshian S (2004) Improved streamflow forecasting using self-organizing radial basis function artificial neural networks. J Hydrol 295:246–262
Muttiah RS, Srinivasan R, Allen PM (1997) Prediction of two-year peak stream discharges using neural networks. J Am Water Resour Assoc 33:625–630
NCDC (2004) Weather data for Victoria, TX, accessed: 08/2004: http://www.ncdc.gov
Principie JC, Euliano NR, Lefebvre WC (2000) Neural and adaptive systems—fundamentals through simulation. John Wiley and Sons, New York
Rajurkar MP, Kothyari UC, Chaube UC (2004) Modeling of the daily rainfall-runoff relationship with artificial neural network. J Hydrol 285:96–113
Thirumalaiah K, Deo MC (1998) River stage forecasting using artificial neural networks. J Hydrol Eng 3:26–32
Tokar AS, Markus M (2000) Precipitation-runoff modeling using artificial neural networks and conceptual models. J Hydrol Eng 5:156–161
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Financial support for the National Oceanic and Atmospheric Administration (NOAA) through a cooperative agreement to Texas A&M University-Kingsville is greatly appreciated.
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Uddameri, V. Using statistical and artificial neural network models to forecast potentiometric levels at a deep well in South Texas. Environ Geol 51, 885–895 (2007). https://doi.org/10.1007/s00254-006-0452-5
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DOI: https://doi.org/10.1007/s00254-006-0452-5