Forecasting PM10 levels using ANN and MLR: A case study for Sakarya City

被引：33

作者：

Ceylan, Z. ^{[1
,2
]}

Bulkan, S. ^{[1
]}

机构：

[1] Marmara Univ, Ind Engn Dept, Fac Engn, TR-34722 Istanbul, Turkey

[2] Ondokuz Mayis Univ, Ind Engn Dept, Fac Engn, TR-55139 Samsun, Turkey

来源：

GLOBAL NEST JOURNAL | 2018年 / 20卷 / 02期

关键词：

Particulate matter; PM10; prediction; artificial neural network; multi-linear regression; ARTIFICIAL NEURAL-NETWORKS; AIR-POLLUTION; MODEL; PREDICTION; PM2.5; EXPOSURE;

D O I：

10.30955/gnj.002522

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

In this study, potential of neural network to estimate daily mean PM10 concentration levels in Sakarya city, Turkey as a case study was examined to achieve improved prediction ability. The level and distribution of air pollutants in a particular region is associated with changes in meteorological conditions affecting air movements and topographic features. Thus, meteorological variables data for a two-year period for Sakarya city which is located in most industrialized and crowded part of Turkey were selected as input. Neural network models and multiple linear regression models have been statistically evaluated. The results of the study showed that ANN models were accurate enough for prediction of PM10 levels.

引用

页码：281 / 290

页数：10

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