This paper introduces an application of machine learning, on real data. It deals with Ensemble Modeling, a simple averaging method for obtaining more reliable approximations using symbolic regression. Considerations on the contribution of bias and variance to the total error, and ensemble methods to reduce errors due to variance, have been tackled together with a specific application of ensemble modeling to hydrological forecasts. This work provides empirical evidence that genetic programming can greatly benefit from this approach in forecasting and simulating physical phenomena. Further considerations have been taken into account, such as the influence of Genetic Programming parameter settings on the model's performance.
Research Article|March 01 2007
Ensemble modeling approach for rainfall/groundwater balancing
1Department of Civil and Environmental Engineering, Technical University of Bari, Viale del Turismo 8Taranto, 74100, Italy
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Journal of Hydroinformatics (2007) 9 (2): 95-106.
D. Laucelli, O. Giustolisi, V. Babovic, M. Keijzer; Ensemble modeling approach for rainfall/groundwater balancing. Journal of Hydroinformatics 1 March 2007; 9 (2): 95–106. doi: https://doi.org/10.2166/hydro.2007.102
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