Computational Intelligence-based PM2.5 Air Pollution Forecasting

被引:12
|
作者
Oprea, M. [1 ]
Mihalache, S. F. [1 ]
Popescu, M. [1 ]
机构
[1] Petr Gas Univ Ploiesti, Automat Control Comput & Elect Dept, Bd Bucuresti 39, Ploiesti 100680, Romania
关键词
computational intelligence; PM2.5 air pollution forecasting; ANFIS; ANN; ANN architecture identification; ARTIFICIAL NEURAL-NETWORKS; FINE PARTICULATE MATTER; PREDICTION; MODEL; SERIES; REGRESSION; SYSTEM; PM10;
D O I
10.15837/ijccc.2017.3.2907
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Computational intelligence based forecasting approaches proved to be more efficient in real time air pollution forecasting systems than the deterministic ones that are currently applied. Our research main goal is to identify the computational intelligence model that is more proper to real time PM2.5 air pollutant forecasting in urban areas. Starting from the study presented in [27](a), in this paper we first perform a comparative study between the most accurate computational intelligence models that were used for particulate matter (fraction PM2.5) air pollution forecasting: artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS). Based on the obtained experimental results, we make a comprehensive analysis of best ANN architecture identification. The experiments were realized on datasets from the AirBase databases with PM2.5 concentration hourly measurements. The statistical parameters that were computed are mean absolute error, root mean square error, index of agreement and correlation coefficient.
引用
收藏
页码:365 / 380
页数:16
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