A deep multitask learning approach for air quality prediction

DOI10.1007/s10479-020-03734-1OpenAlexW3045708591MaRDI QIDQ2241161

Xiaotong Sun, Qili Wang, Hongxun Jiang, Wei Xu

Publication date: 8 November 2021

Published in: Annals of Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10479-020-03734-1

zbMATH Keywords

deep learningmultitask learningsmart cityair quality forecastspatial-temporal analysis

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Functional-differential equations (including equations with delayed, advanced or state-dependent argument) (34Kxx) Inference from stochastic processes (62Mxx)

Related Items (1)

OR and analytics for digital, resilient, and sustainable manufacturing 4.0

Uses Software

• WRF-Chem
• CALPUFF

Cites Work

• Greedy function approximation: A gradient boosting machine.
• Modelling counter-intuitive effects on cost and air pollution from intermittent generation
• Forecasting concentrations of air pollutants by logarithm support vector regression with immune algorithms
• Neural networks in financial trading
• A Bayesian/information theoretic model of learning to learn via multiple task sampling
• Multi-target regression via input space expansion: treating targets as inputs
• Anti-periodic solutions for state-dependent impulsive recurrent neural networks with time-varying and continuously distributed delays
• Massive datasets and machine learning for computational biomedicine: trends and challenges

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