Air Pollution Monitoring Using WSN Nodes with Machine Learning Techniques: A Case Study

Autor:	Ricardo P. Arciniega-Rocha, Diego Hernán Peluffo-Ordóñez, Vivian F. López-Batista, Paul D. Rosero-Montalvo
Rok vydání:	2021
Předmět:	Logic Computer science 010401 analytical chemistry 0502 economics and business 05 social sciences Real-time computing Air pollution medicine medicine.disease_cause 01 natural sciences 050203 business & management 0104 chemical sciences
Zdroj:	Logic Journal of the IGPL. 30:599-610
ISSN:	1368-9894 1367-0751
Popis:	Air pollution is a current concern of people and government entities. Therefore, in urban scenarios, its monitoring and subsequent analysis is a remarkable and challenging issue due mainly to the variability of polluting-related factors. For this reason, the present work shows the development of a wireless sensor network that, through machine learning techniques, can be classified into three different types of environments: high pollution levels, medium pollution and no noticeable contamination into the Ibarra City. To achieve this goal, signal smoothing stages, prototype selection, feature analysis and a comparison of classification algorithms are performed. As relevant results, there is a classification performance of 95% with a significant noisy data reduction.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::63c4227bcb96a180184d375a234bbffc https://doi.org/10.1093/jigpal/jzab005 Zobrazit plný text záznamu