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Energy Sources, Part B: Economics, Planning, and Policy Volume 1, 2006 - Issue 2
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Original Articles

Forecasting Ambient Air SO2 Concentrations Using Artificial Neural Networks

Sait C. Sofuoglu Izmir Institute of Technology, Department of Chemical Engineering, Gülbahçe Köyü, Urla, Izmir, Turkey
,
Aysun Sofuoglu Izmir Institute of Technology, Department of Chemical Engineering, Gülbahçe Köyü, Urla, Izmir, Turkey
,
Savas Birgili Izmir Institute of Technology, Environmental Engineering MS Program, Gülbahçe Köyü, Urla, Izmir, Turkey
&
Gokmen Tayfur Izmir Institute of Technology, Department of Civil Engineering, Gülbahçe Köyü, Urla, Izmir, Turkey
Pages 127-136 | Published online: 22 Sep 2006

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Serdar Gündoğdu. (2020) Comparison of static MLP and dynamic NARX neural networks for forecasting of atmospheric PM10 and SO2 concentrations in an industrial site of Turkey. Environmental Forensics 21:3-4, pages 363-374.
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S John Livingston, S. Deepa Kanmani, A. Shamila Ebenezer, Dahlia Sam & A. Joshi. (2023) An ensembled method for air quality monitoring and control using machine learning. Measurement: Sensors 30, pages 100914.
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Debendra Dalai, Sanjay Sharma, Varinder Kanwar & Jyotsna Kaushal. 2022. Proceedings of International Conference on Innovative Technologies for Clean and Sustainable Development (ICITCSD – 2021). Proceedings of International Conference on Innovative Technologies for Clean and Sustainable Development (ICITCSD – 2021) 791 800 .
Jonghak Lee, Sangil Kwon, Hyungjun Kim, Jihoon Keel, Taekwan Yoon & Jongtae Lee. (2021) Machine Learning Applied to the NOx Prediction of Diesel Vehicle under Real Driving Cycle. Applied Sciences 11:9, pages 3758.
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Kaan Yetilmezsoy. 2019. Handbook of Environmental Materials Management. Handbook of Environmental Materials Management 2001 2046 .
Hao Lyu, Tie Dai, Youfei Zheng, Guangyu Shi & Teruyuki Nakajima. (2018) Estimation of PM<sub>2.5</sub> Concentrations over Beijing with MODIS AODs Using an Artificial Neural Network. SOLA 14:0, pages 14-18.
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Kaan Yetilmezsoy. 2018. Handbook of Environmental Materials Management. Handbook of Environmental Materials Management 1 47 .
Xiang'zi Leng, Jinhua Wang, Haibo Ji, Qin'geng Wang, Huiming Li, Xin Qian, Fengying Li & Meng Yang. (2017) Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses. Chemosphere 180, pages 513-522.
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Maher Elbayoumi, Nor Azam Ramli & Noor Faizah Fitri Md Yusof. (2015) Development and comparison of regression models and feedforward backpropagation neural network models to predict seasonal indoor PM2.5–10 and PM2.5 concentrations in naturally ventilated schools. Atmospheric Pollution Research 6:6, pages 1013-1023.
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Yerong Wu, Jianping Guo, Xiaoye Zhang, Xin Tian, Jiahua Zhang, Yaqiang Wang, Jing Duan & Xiaowen Li. (2012) Synergy of satellite and ground based observations in estimation of particulate matter in eastern China. Science of The Total Environment 433, pages 20-30.
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Yerong Wu, Jianping Guo, Xin Zhang & Xiaowen Li. (2011) Correlation between PM concentrations and Aerosol Optical Depth in eastern China based on BP neural networks. Correlation between PM concentrations and Aerosol Optical Depth in eastern China based on BP neural networks.
Hui Xie, Fei Ma & Qingyuan Bai. (2009) Prediction of Indoor Air Quality Using Artificial Neural Networks. Prediction of Indoor Air Quality Using Artificial Neural Networks.
Luis A. Díaz-Robles, Juan C. Ortega, Joshua S. Fu, Gregory D. Reed, Judith C. Chow, John G. Watson & Juan A. Moncada-Herrera. (2008) A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile. Atmospheric Environment 42:35, pages 8331-8340.
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Sait C. Sofuoglu. (2008) Application of artificial neural networks to predict prevalence of building-related symptoms in office buildings. Building and Environment 43:6, pages 1121-1126.
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