Lazy Learning based surrogate models for air quality planning

By C. Carnevale, G. Finzi, A. Pederzoli, E. Turrini, M. Volta. 2016

28 citations recorded by the source corpus.

Subfields: Automotive Engineering, Environmental Engineering, Health, Toxicology and Mutagenesis

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Octavio Loyola-González
2. Ulrich Gunter
3. Ricardo J. Bessa

In Environmental Engineering

1. Octavio Loyola-González
2. Christoph Bergmeir
3. Thordis Linda Thorarinsdottir
4. Tim Januschowski
5. Pierre Pinson
6. Ricardo J. Bessa
7. Florian Ziel
8. Jooyoung Jeon

In Health, Toxicology and Mutagenesis

1. Octavio Loyola-González

Citations in the corpus

• Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, methodology): “Lazy learning-based approach has widely been used in several practical contexts, such as data streams [87], air quality planning [88], predictive toxicology based on a chemical ontology [89], prediction of customer demands [90], hydrologic forecasting [91], air quality prediction [92], magne-”
• Forecasting: theory and practice (2020, background): “Clements and Galvão (2012, 2013a) and Carriero et al. (2015) minimise the effects of benchmark revisions and re-basing by modelling ‘samevintage-growth rates’, namely Zt+1 = ( yt+1 t , y t+1 t−1 , .”