An empirical comparison of machine learning techniques for dam behaviour modelling
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Open Access
- 1 September 2015
- journal article
- research article
- Published by Elsevier in Structural Safety
- Vol. 56, 9-17
- https://doi.org/10.1016/j.strusafe.2015.05.001
Abstract
No abstract availableKeywords
Funding Information
- Spanish Ministry of Economy and Competitiveness (Ministerio de Economía y Competitividad, MINECO) (IPT-2012-0813-390000)
- AIDA (BIA2013-49018-C2-1-R, BIA2013-49018-C2-2-R)
This publication has 17 references indexed in Scilit:
- Modelling of dam behaviour based on neuro-fuzzy identificationEngineering Structures, 2012
- Predictive models for forecasting hourly urban water demandJournal of Hydrology, 2010
- Variable selection using random forestsPattern Recognition Letters, 2010
- SmcHD1, containing a structural-maintenance-of-chromosomes hinge domain, has a critical role in X inactivationNature Genetics, 2008
- Impulse response function analysis of pore pressures in earthdamsEuropean Journal of Environmental and Civil Engineering, 2008
- Statistical analysis and structural identification in concrete dam monitoringEngineering Structures, 2007
- Support Vector Machines with ApplicationsStatistical Science, 2006
- Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001IEEE Transactions on Power Systems, 2004
- Greedy function approximation: A gradient boosting machine.The Annals of Statistics, 2001
- Multivariate Adaptive Regression SplinesThe Annals of Statistics, 1991