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Scientific Article details

Title Pollution and Weather Reports: Using Machine Learning for Combating Pollution in Big Cities
ID_Doc 43070
Authors Popa, CL; Dobrescu, TG; Silvestru, CI; Firulescu, AC; Popescu, CA; Cotet, CE
Title Pollution and Weather Reports: Using Machine Learning for Combating Pollution in Big Cities
Year 2021
Published Sensors, 21, 21
DOI 10.3390/s21217329
Abstract Air pollution has become the most important issue concerning human evolution in the last century, as the levels of toxic gases and particles present in the air create health problems and affect the ecosystems of the planet. Scientists and environmental organizations have been looking for new ways to combat and control the air pollution, developing new solutions as technologies evolves. In the last decade, devices able to observe and maintain pollution levels have become more accessible and less expensive, and with the appearance of the Internet of Things (IoT), new approaches for combating pollution were born. The focus of the research presented in this paper was predicting behaviours regarding the air quality index using machine learning. Data were collected from one of the six atmospheric stations set in relevant areas of Bucharest, Romania, to validate our model. Several algorithms were proposed to study the evolution of temperature depending on the level of pollution and on several pollution factors. In the end, the results generated by the algorithms are presented considering the types of pollutants for two distinct periods. Prediction errors were highlighted by the RMSE (Root Mean Square Error) for each of the three machine learning algorithms used.
Author Keywords pollution; sensors; machine learning; smart city
Index Keywords Index Keywords
Document Type Other
Open Access Open Access
Source Science Citation Index Expanded (SCI-EXPANDED)
EID WOS:000718516600001
WoS Category Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation
Research Area Chemistry; Engineering; Instruments & Instrumentation
PDF https://www.mdpi.com/1424-8220/21/21/7329/pdf?version=1636525553
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