A novel approach to forecast urban surface-level ozone considering heterogeneous locations and limited information-Reference-Cited by-同舟云学术

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A novel approach to forecast urban surface-level ozone considering heterogeneous locations and limited information

Published:2018-12 Issue: Volume:110 Page:52-61
ISSN:1364-8152
Container-title:Environmental Modelling & Software
language:en
Short-container-title:Environmental Modelling & Software

Author:

Gómez-Losada Álvaro,Asencio–Cortés G.,Martínez–Álvarez F.,Riquelme J.C.

Funder

Spanish Ministry of Economy and Competitiveness

Junta de Andalucía

Publisher

Elsevier BV

Subject

Ecological Modelling,Environmental Engineering,Software

Reference31 articles.

1. PSF: introduction to R Package for pattern sequence based forecasting algorithm;Bokde;Rice J.,2017

2. Random forests;Breiman;Mach. Learn.,2001

3. Efficient prediction of total column ozone based on support vector regression algorithms, numerical models and Suomi-satellite data;Carro-Calvo;Atmósfera,2017

4. Artificial neural network with back-propagation learning to predict mean monthly total ozone in Arosa, Switzerland;Chattopadhyay;Int. J. Rem. Sens.,2007

5. Air pollution prediction via multi-label classification;Corani;Environ. Model. Software,2016

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