Predicting pollution incidents through semiparametric quantile regression models

Functional Location-Scale Model to Forecast Bivariate Pollution Episodes

“…Table 1. Selected models from equation in (11) . Cross X indicates the covariates included in each of the four considered models.…”

“…The results correspond to curves observed in ten points (t lag = 10) and represented in a basis expansion in three functional principal components. These univariate confidence intervals were respectively constructed from (11) Table 4 shows the maximum consumed memory and the runtime (in seconds) for the four models tested and two different dimensions of the submatrices are executed in a Intel Core i7-2600K with 16 GB of RAM. Table 4.…”

“…Forecasting air quality and concentrations of pollutants in the atmosphere by means of statistical methods is an active area of research given the transcendence of the problem and the difficulty to find optimal solutions using deterministic mathematical models. Among the different methods that can be found in the literature to tackle this problem, models for time series analysis such as the integrated autoregressive moving average-ARIMA [1][2][3], multivariate regression [4][5][6][7], generalized linear or additive models (GAM) [8][9][10][11] and artificial neural networks (ANN) [12][13][14][15][16][17][18][19] are the most extended. Due to the increased access to continuous data over time, functional data analysis [20,21] was also proposed for air quality forecasting and outlier detection [22][23][24].…”

“…Specifically, this is the purpose of our work: forecasting pollution episodes of SO 2 and NO x early enough to allow corrective measures to be taken. Our approach uses a location-scale model [11,38,39] that treats the predictors, the concentrations of both pollutants over time, as functions, while the response is a scalar, the concentration of the pollutants some time in advance. The novelty of our approach is the combination of a biviariate location-scale model with functional additive models.…”

Multifractal Characteristics on Temporal Maximum of Air Pollution Series

“…One of the most important processes in monitoring and assessing air pollution behaviors is the analysis of recorded air pollution data over time. Most of the available literature investigates the behaviors of air pollution data using various statistical models, including the time series approach [9][10][11][12], regression technique [13,14], stochastic analysis [15,16], distribution models [17][18][19], neural network and deep learning [20][21][22], spatial-temporal [23][24][25], extreme-value analysis [26,27], and multivariate approach [28,29]. All of these methods provide valuable information about the behaviors, trends, and dependency structures of air pollution characteristics.…”