Zobrazeno 1 - 10
of 202
pro vyhledávání: '"Cameletti, Michela"'
Random Forest (RF) is a widely used machine learning algorithm known for its flexibility, user-friendliness, and high predictive performance across various domains. However, it is non-interpretable. This can limit its usefulness in applied sciences,
Externí odkaz:
http://arxiv.org/abs/2408.05537
Autor:
Salis, Matteo, Zucchi, Andrea, FUSTA MORO, ALESSANDRO, Cameletti, Michela, GOLINI, Natalia, Ignaccolo, Rosaria
Lombardy is one of the most polluted regions at the European level, also due to its particular geographical structure and weather conditions which prevent the pollutants’ dispersion, and the high levels of emissions coming from human activities. Re
Externí odkaz:
https://library.oapen.org/handle/20.500.12657/74912
Autor:
Otto, Philipp, Moro, Alessandro Fusta, Rodeschini, Jacopo, Shaboviq, Qendrim, Ignaccolo, Rosaria, Golini, Natalia, Cameletti, Michela, Maranzano, Paolo, Finazzi, Francesco, Fassò, Alessandro
This study presents a comparative analysis of three predictive models with an increasing degree of flexibility: hidden dynamic geostatistical models (HDGM), generalised additive mixed models (GAMM), and the random forest spatiotemporal kriging models
Externí odkaz:
http://arxiv.org/abs/2309.07285
Random Forest (RF) is a well-known data-driven algorithm applied in several fields thanks to its flexibility in modeling the relationship between the response variable and the predictors, also in case of strong non-linearities. In environmental appli
Externí odkaz:
http://arxiv.org/abs/2303.04693
Autor:
Palmí-Perales, Francisco, Gómez-Rubio, Virgilio, Bivand, Roger S, Cameletti, Michela, Rue, Håvard
Bayesian methods and software for spatial data analysis are generally now well established in the scientific community. Despite the wide application of spatial models, the analysis of multivariate spatial data using R-INLA has not been widely describ
Externí odkaz:
http://arxiv.org/abs/2212.10976
Autor:
Fassò, Alessandro, Rodeschini, Jacopo, Moro, Alessandro Fusta, Shaboviq, Qendrim, Maranzano, Paolo, Cameletti, Michela, Finazzi, Francesco, Golini, Natalia, Ignaccolo, Rosaria, Otto, Philipp
Publikováno v:
Scientific Data 10(143) 2023
The air in the Lombardy region, Italy, is one of the most polluted in Europe because of limited air circulation and high emission levels. There is a large scientific consensus that the agricultural sector has a significant impact on air quality. To s
Externí odkaz:
http://arxiv.org/abs/2210.10604
Autor:
Konstantinoudis, Garyfallos, Gómez-Rubio, Virgilio, Cameletti, Michela, Pirani, Monica, Baio, Gianluca, Blangiardo, Marta
COVID-19 related deaths underestimate the pandemic burden on mortality because they suffer from completeness and accuracy issues. Excess mortality is a popular alternative, as it compares observed with expected deaths based on the assumption that the
Externí odkaz:
http://arxiv.org/abs/2201.06458
When a new environmental policy or a specific intervention is taken in order to improve air quality, it is paramount to assess and quantify - in space and time - the effectiveness of the adopted strategy. The lockdown measures taken worldwide in 2020
Externí odkaz:
http://arxiv.org/abs/2110.15020
This paper illustrates the main results of a spatio-temporal interpolation process of $\text{PM}_{10}$ concentrations at daily resolution using a set of 410 monitoring sites, distributed throughout the Italian territory, for the year 2015. The interp
Externí odkaz:
http://arxiv.org/abs/2009.10476