Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru
The prediction of air pollution is of great importance in highly populated areas because it directly impacts both the management of the city's economic activity and the health of its inhabitants. This work evaluates and predicts the Spatio-temporal behavior of air quality in Metropolitan Lima,...
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| Formato: | Artículo |
| Lenguaje: | inglés |
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2021
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| author | Cordova, Chardin Hoyos Lopez Portocarrero, Manuel Niño Salas, Rodrigo Torres, Romina Rodrigues, Paulo Canas López Gonzales, Javier Linkolk |
| author_browse | Cordova, Chardin Hoyos Lopez Portocarrero, Manuel Niño López Gonzales, Javier Linkolk Rodrigues, Paulo Canas Salas, Rodrigo Torres, Romina |
| author_facet | Cordova, Chardin Hoyos Lopez Portocarrero, Manuel Niño Salas, Rodrigo Torres, Romina Rodrigues, Paulo Canas López Gonzales, Javier Linkolk |
| author_sort | Cordova, Chardin Hoyos |
| collection | Producción científica |
| description | The prediction of air pollution is of great importance in highly populated areas because it directly impacts both the management of the city's economic activity and the health of its inhabitants. This work evaluates and predicts the Spatio-temporal behavior of air quality in Metropolitan Lima, Peru, using artificial neural networks. The conventional feedforward backpropagation known as Multilayer Perceptron (MLP) and the Recurrent Artificial Neural network known as Long Short-Term Memory networks (LSTM) were implemented for the hourly prediction of [Formula: see text] based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. The models were validated using two schemes: The Hold-Out and the Blocked-Nested Cross-Validation (BNCV). The simulation results show that periods of moderate [Formula: see text] concentration are predicted with high precision. Whereas, for periods of high contamination, the performance of both models, the MLP and LSTM, were diminished. On the other hand, the prediction performance improved slightly when the models were trained and validated with the BNCV scheme. The simulation results showed that the models obtained a good performance for the CDM, CRB, and SMP monitoring stations, characterized by a moderate to low level of contamination. However, the results show the difficulty of predicting this contaminant in those stations that present critical contamination episodes, such as ATE and HCH. In conclusion, the LSTM recurrent artificial neural networks with BNCV adapt more precisely to critical pollution episodes and have better predictability performance for this type of environmental data. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/1337 |
| institution | UPeU |
| language | English |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/13372026-10-30T16:15:36ZAir quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-PeruCordova, Chardin HoyosLopez Portocarrero, Manuel NiñoSalas, RodrigoTorres, RominaRodrigues, Paulo CanasLópez Gonzales, Javier LinkolkEnvironmental sciencesEnvironmental social sciencesThe prediction of air pollution is of great importance in highly populated areas because it directly impacts both the management of the city's economic activity and the health of its inhabitants. This work evaluates and predicts the Spatio-temporal behavior of air quality in Metropolitan Lima, Peru, using artificial neural networks. The conventional feedforward backpropagation known as Multilayer Perceptron (MLP) and the Recurrent Artificial Neural network known as Long Short-Term Memory networks (LSTM) were implemented for the hourly prediction of [Formula: see text] based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. The models were validated using two schemes: The Hold-Out and the Blocked-Nested Cross-Validation (BNCV). The simulation results show that periods of moderate [Formula: see text] concentration are predicted with high precision. Whereas, for periods of high contamination, the performance of both models, the MLP and LSTM, were diminished. On the other hand, the prediction performance improved slightly when the models were trained and validated with the BNCV scheme. The simulation results showed that the models obtained a good performance for the CDM, CRB, and SMP monitoring stations, characterized by a moderate to low level of contamination. However, the results show the difficulty of predicting this contaminant in those stations that present critical contamination episodes, such as ATE and HCH. In conclusion, the LSTM recurrent artificial neural networks with BNCV adapt more precisely to critical pollution episodes and have better predictability performance for this type of environmental data.Articlehttps://pubmed.ncbi.nlm.nih.gov/34930975https://www.nature.com/articles/s41598-021-03650-9.pdfhttps://doi.org/10.1038/s41598-021-03650-9PMC868854510.1038/s41598-021-03650-9https://hdl.handle.net/20.500.15037/133734930975W4200457728enScientific Reports2045-23222021-12-202026-06-13T09:53:47Z2026-06-13T09:53:47Z |
| spellingShingle | Environmental sciences Environmental social sciences Cordova, Chardin Hoyos Lopez Portocarrero, Manuel Niño Salas, Rodrigo Torres, Romina Rodrigues, Paulo Canas López Gonzales, Javier Linkolk Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru |
| title | Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru |
| title_full | Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru |
| title_fullStr | Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru |
| title_full_unstemmed | Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru |
| title_short | Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru |
| title_sort | air quality assessment and pollution forecasting using artificial neural networks in metropolitan lima peru |
| topic | Environmental sciences Environmental social sciences |
| topic_facet | Environmental sciences Environmental social sciences |
| url | https://pubmed.ncbi.nlm.nih.gov/34930975 https://www.nature.com/articles/s41598-021-03650-9.pdf https://doi.org/10.1038/s41598-021-03650-9 https://hdl.handle.net/20.500.15037/1337 |
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