PM2.5 concentrations forecasting using a new multi-objective feature selection and ensemble framework-Reference-Cited by-同舟云学术

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PM2.5 concentrations forecasting using a new multi-objective feature selection and ensemble framework

Published:2020-07 Issue:7 Volume:11 Page:1187-1198
ISSN:1309-1042
Container-title:Atmospheric Pollution Research
language:en
Short-container-title:Atmospheric Pollution Research

Author:

Wu Haiping,Liu Hui,Duan Zhu

Funder

National Natural Science Foundation of China

Changsha Science & Technology Project

Central South University

Publisher

Elsevier BV

Subject

Atmospheric Science,Pollution,Waste Management and Disposal

Reference43 articles.

1. A new hybrid enhanced local linear neuro-fuzzy model based on the optimized singular spectrum analysis and its application for nonlinear and chaotic time series forecasting;Abdollahzade;Inf. Sci.,2015

2. A new feature selection technique for load and price forecast of electrical power systems;Abedinia;IEEE Trans. Power Syst.,2017

3. Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia;Al-Musaylh;Adv. Eng. Inf.,2018

4. LIBSVM: a library for support vector machines;Chang;ACM transactions on intelligent systems and technology (TIST),2011

5. Assessment of aerosol types on improving the estimation of surface PM2.5 concentrations by using ground-based aerosol optical depth dataset;Chen;Atmospheric Pollution Research,2019

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2. Modeling PM2.5 forecast using a self-weighted ensemble GRU network: Method optimization and evaluation;Ecological Indicators;2023-12

3. An Ensemble Model for PM2.5 Concentration Prediction Based on Feature Selection and Two-Layer Clustering Algorithm;Atmosphere;2023-09-25

4. A multi-step ahead point-interval forecasting system for hourly PM2.5 concentrations based on multivariate decomposition and kernel density estimation;Expert Systems with Applications;2023-09

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