A Comparative Study of Computational Intelligence Techniques Applied to PM2.5 Air Pollution Forecasting

被引：0

作者：

Oprea, Mihaela ^{[1
]}

Mihalache, Sanda Florentina ^{[1
]}

Popescu, Marian ^{[1
]}

机构：

[1] Petr Gas Univ Ploiesti, Automat Control Comp & Elect Dept, Ploiesti, Romania

来源：

2016 6TH INTERNATIONAL CONFERENCE ON COMPUTERS COMMUNICATIONS AND CONTROL (ICCCC) | 2016年

关键词：

computational intelligence; ANN; ANFIS; particulate matter; air pollution forecasting; ARTIFICIAL NEURAL-NETWORKS; PREDICTION; MODEL; PM10;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The paper presents the results of a comparative study performed between two computational intelligence techniques, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) applied to particulate matter (fraction PM2.5) air pollution forecasting. The experiments were realized on datasets from the Airbase databases with PM2.5 hourly measurements. The main statistical parameters that were computed are root mean square error (RMSE) and mean absolute error (MAE).

引用

页码：103 / 108

页数：6

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