A hybrid air quality early-warning framework: An hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms-Reference-Cited by-同舟云学术

A hybrid air quality early-warning framework: An hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms

Published:2020-03 Issue: Volume:709 Page:135934
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Sharma Ekta,Deo Ravinesh C.^ORCID,Prasad Ramendra,Parisi Alfio V.

Funder

Australian Mathematical Sciences Institute

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference132 articles.

1. Application of artificial neural networks to rainfall forecasting in Queensland, Australia;Abbot;Adv. Atmos. Sci.,2012

2. Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks;Abbot;Atmos. Res.,2014

3. The Burden of Disease and Injury in Australia 2003;AIHW,2007

4. Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition;Ali;Renew. Sust. Energ. Rev.,2019

5. Multi-stage hybridized online sequential extreme learning machine integrated with Markov Chain Monte Carlo copula-Bat algorithm for rainfall forecasting;Ali;Atmos. Res.,2018

Cited by 80 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. BWO-BiLSTM & CNN composite model for prediction of atmospheric particulate matter mass concentration;Atmospheric Pollution Research;2024-11

2. An Evaluation of the Influence of Meteorological Factors and a Pollutant Emission Inventory on PM2.5 Prediction in the Beijing–Tianjin–Hebei Region Based on a Deep Learning Method;Environments;2024-05-23

3. Improving PM10 and PM2.5 concentration prediction using the Brazilian Regional Atmospheric Modeling 5.2 System in Lima, Peru;Urban Climate;2024-05

4. Data analysis and preprocessing techniques for air quality prediction: a survey;Stochastic Environmental Research and Risk Assessment;2024-03-18

5. Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania;Sensors;2024-02-27