A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer
Top Cited Papers
- 11 November 2010
- journal article
- Published by Elsevier
- Vol. 396 (1-2), 128-138
- https://doi.org/10.1016/j.jhydrol.2010.11.002
Abstract
No abstract availableKeywords
This publication has 37 references indexed in Scilit:
- Forecasting solute breakthrough curves through the unsaturated zone using artificial neural networksPublished by Elsevier ,2006
- Support vector regression for real-time flood stage forecastingJournal of Hydrology, 2006
- Multi-time scale stream flow predictions: The support vector machines approachPublished by Elsevier ,2005
- Applicability of statistical learning algorithms in groundwater quality modelingWater Resources Research, 2005
- Uncertainty in the calibration of effective roughness parameters in HEC-RAS using inundation and downstream level observationsJournal of Hydrology, 2005
- Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodologyPublished by Elsevier ,2001
- Daily reservoir inflow forecasting using artificial neural networks with stopped training approachPublished by Elsevier ,2000
- Calibration of transfer function–noise models to sparsely or irregularly observed time seriesWater Resources Research, 1999
- An extension of Box-Jenkins transfer/noise models for spatial interpolation of groundwater head seriesJournal of Hydrology, 1997
- Comparison of univariate and transfer function models of groundwater fluctuationsWater Resources Research, 1993