Prediction of SO2 and PM10 air pollutants using a deep learning-based recurrent neural network: Case of industrial city Sakarya
Author:
Publisher
Elsevier BV
Subject
Atmospheric Science,Urban Studies,Environmental Science (miscellaneous),Geography, Planning and Development
Reference23 articles.
1. Evaluation of key parameters using deep convolutional neural networks for airborne pollution (PM10) prediction;Aceves-Fernández,2020
2. Estimation of air pollutants emission (PM10, CO, SO2 and NOx) during development of the industry using AUSTAL 2000 model: a new method for sustainable development;Atamaleki;MethodsX.,2019
3. A deep learning framework for financial time series using stacked autoencoders and long-short term memory;Bao;PLoS ONE.,2017
4. Forecasting PM10 levels using ANN and MLR: A case study for Sakarya City;Ceylan;Global Nest J.,2018
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