Jump to Main Content
- García Nieto, P.J.; García-Gonzalo, E.; Bernardo Sánchez, A.; Rodríguez Miranda, A.A.
- Environmental modeling and assessment 2018 v.23 no.3 pp. 229-247
- air quality; algorithms; carbon monoxide; data collection; dust; metropolitan areas; neural networks; nitrogen oxides; ozone; prediction; regression analysis; sulfur dioxide; Spain
- ... The main aim of this study was to construct several regression models of air quality using techniques based on the statistical learning, in the metropolitan area of Oviedo, in northern Spain. In this research, a hybrid particle swarm optimization-based evolutionary support vector regression is implemented to predict the air quality from the experimental dataset (specifically, nitrogen oxides, carb ...
- El Moçayd, Nabil; Ricci, Sophie; Goutal, Nicole; Rochoux, MélanieC.; Boyaval, Sébastien; Goeury, Cédric; Lucor, Didier; Thual, Olivier
- Environmental modeling and assessment 2018 v.23 no.3 pp. 309-331
- Monte Carlo method; cost effectiveness; equations; friction; geometry; hydrodynamics; models; prediction; probability distribution; rivers; uncertainty; variance covariance matrix; water management; France
- ... Assessing epistemic uncertainties is considered as a milestone for improving numerical predictions of a dynamical system. In hydrodynamics, uncertainties in input parameters translate into uncertainties in simulated water levels through the shallow water equations. We investigate the ability of generalized polynomial chaos (gPC) surrogate to evaluate the probabilistic features of water level simul ...