Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru - Chardin Hoyos Cordova; Manuel Niño Lopez Portocarrero; Rodrigo Salas; Romina Torres; Paulo Canas Rodrigues; Javier Linkolk López‐Gonzales (My University, 2021)
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.
Keywords: Artificial neural network; Predictability; Computer science; Multilayer perceptron; Air quality index; Metropolitan area; Pollution; Recurrent neural network; Backpropagation; Feed forward; Feedforward neural network; Machine learning; Air pollution; Artificial intelligence; Environmental science; Meteorology; Statistics; Mathematics; Geography; Engineering; Organic chemistry; Chemistry; Control engineering; Biology; Ecology; Archaeology; Artificial neural network; Predictability; Computer science; Multilayer perceptron; Air quality index; Metropolitan area; Pollution; Recurrent neural network; Backpropagation; Feed forward; Feedforward neural network; Machine learning; Air pollution; Artificial intelligence; Environmental science; Meteorology; Statistics; Mathematics; Geography; Engineering; Physical Sciences Environmental Science Environmental Engineering Air Quality Monitoring and Forecasting; Physical Sciences Environmental Science Health, Toxicology and Mutagenesis Air Quality and Health Impacts; Physical Sciences Engineering Automotive Engineering Vehicle emissions and performance