Urban Air Quality Forecasting: A Regression and a Classification Approach

被引：1

作者：

Karatzas, Kostas ^{[1
]}

Katsifarakis, Nikos ^{[1
]}

Orlowski, Cezary ^{[2
]}

Sarzynski, Arkadiusz ^{[3
]}

机构：

[1] Aristotle Univ Thessaloniki, Dept Mech Engn, Informat Syst & Applicat, Environm Informat Res Grp, Thessaloniki, Greece

[2] WSB Univ Gdansk, Inst Management & Finance, Gdansk, Poland

[3] Gdansk Univ Technol, Dept Appl Business Informat, Fac Econ & Management, Gdansk, Poland

来源：

INTELLIGENT INFORMATION AND DATABASE SYSTEMS (ACIIDS 2017), PT II | 2017年 / 10192卷

关键词：

Computational intelligence; Air pollution; Classification; Regression; Ensemble; POLLUTION;

D O I：

10.1007/978-3-319-54430-4_52

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We employ Computational Intelligence (CI) methods to model air pollution for the Greater Gdansk Area in Poland. The forecasting problem is addressed with both classification and regression algorithms. In addition, we present an ensemble method that allows for the use of a single Artificial Neural Network-based model for the whole area of interest. Results indicate good model performance with a correlation coefficient between forecasts and measurements for the hourly PM10 concentration 24 h in advance reaching 0.81 and an agreement index (Cohen's kappa) up to 54%. Moreover, the ensemble model demonstrates a decrease in Mean Square Error in comparison to the best simple model. Overall results suggest that the specific modelling approach can support the provision of air quality forecasts at an operational basis.

引用

页码：539 / 548

页数：10

共 50 条

[31] Relevance Vector Machine with Compounded Kernels for Regression and Classification in Power Systems Forecasting [J].

Duan, Qing ;

Sheng, Wan-Xing ;

Ma, Yan .

2015 12TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD), 2015, :1659-1664

[32] Classification of air quality zones and fine particulate matter sensitive areas by risk assessment approach [J].

Lin, Yuan-Chien ;

Shih, Hua-San ;

Lai, Chun-Yeh .

ENVIRONMENTAL RESEARCH, 2022, 215

[33] A Solution for Air Quality Monitoring and Forecasting [J].

Gruicin, Iasmina ;

Ionascu, Marian-Emanuel ;

Popa, Micca .

2018 IEEE 12TH INTERNATIONAL SYMPOSIUM ON APPLIED COMPUTATIONAL INTELLIGENCE AND INFORMATICS (SACI), 2018, :503-507

[34] gBoost: a mathematical programming approach to graph classification and regression [J].

Hiroto Saigo ;

Sebastian Nowozin ;

Tadashi Kadowaki ;

Taku Kudo ;

Koji Tsuda .

Machine Learning, 2009, 75 :69-89

[35] gBoost: a mathematical programming approach to graph classification and regression [J].

Saigo, Hiroto ;

Nowozin, Sebastian ;

Kadowaki, Tadashi ;

Kudo, Taku ;

Tsuda, Koji .

MACHINE LEARNING, 2009, 75 (01) :69-89

[36] Urban form, traffic volume, and air quality: A spatiotemporal stratified approach [J].

Tian, Ye ;

Yao, Xiaobai .

ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE, 2022, 49 (01) :92-113

[37] A novel dynamic ensemble air quality index forecasting system [J].

Li, Hongmin ;

Wang, Jianzhou ;

Yang, Hufang .

ATMOSPHERIC POLLUTION RESEARCH, 2020, 11 (08) :1258-1270

[38] Forecasting air quality time series using deep learning [J].

Freeman, Brian S. ;

Taylor, Graham ;

Gharabaghi, Bahram ;

The, Jesse .

JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION, 2018, 68 (08) :866-886

[39] Real time air quality forecasting using integrated parametric and non-parametric regression techniques [J].

Donnelly, Aoife ;

Misstear, Bruce ;

Broderick, Brian .

ATMOSPHERIC ENVIRONMENT, 2015, 103 :53-65

[40] An enhanced machine learning model for urban air quality forecasting under intense human activities [J].

Wang, Yelin ;

Xia, Feiyang ;

Yao, Linlin ;

Zhao, Shunyu ;

Li, Youjie ;

Cai, Yanpeng .

URBAN CLIMATE, 2025, 60

← 1 2 3 4 5 →