Modeling of PM10 Air Pollution in Urban Environment Using MARS

Gocheva-Ilieva, Snezhana; Ivanov, Atanas; Voynikova, Desislava Stoyanova; Stoimenova, M.

doi:10.1007/978-3-030-41032-2_27

Cited by 6 publications

(1 citation statement)

References 11 publications

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“…In high dimensional input models, the MARS is a popular nonparametric modeling technique that is provided with a nonlinear map using splines. Recently, MARS as a machine learning tool has had successful applications in complex air pollution predictions, including atmospheric particulate matter as (PM10) [40], (O 3 ) [41], (SO 2 ) [42], (NO 2 ) [43], and Benzene concentration [44]. The MARS could be used to build a nonlinear relationship using the piecewise linear splines basis function (BF) using the following equation:…”

Section: Multivariate Adaptive Regression Spline (Mars)mentioning

confidence: 99%

Modeling Sulphur Dioxide (SO2) Quality Levels of Jeddah City Using Machine Learning Approaches with Meteorological and Chemical Factors

et al. 2022

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Modeling air quality in city centers is essential due to environmental and health-related issues. In this study, machine learning (ML) approaches were used to approximate the impact of air pollutants and metrological parameters on SO2 quality levels. The parameters, NO, NO2, O3, PM10, RH, HyC, T, and P are significant factors affecting air pollution in Jeddah city. These factors were considered as the input parameters of the ANNs, MARS, SVR, and Hybrid model to determine the effect of those factors on the SO2 quality level. Hence, ANN was employed to approximate the nonlinear relation between SO2 and input parameters. The MARS approach has successful applications in air pollution predictions as an ML tool, employed in this study. The SVR approach was used as a nonlinear modeling tool to predict the SO2 quality level. Furthermore, the MARS and SVR approaches were integrated to develop a novel hybrid modeling scheme for providing a nonlinear approximation of SO2 concentration. The main innovation of this hybrid approach applied for predicting the SO2 quality levels is to develop an efficient approach and reduce the time-consuming calibration processes. Four comparative statistical considerations, MAE, RMSE, NSE, and d, were applied to measure the accuracy and tendency. The hybrid SVR model outperforms the other models with the lowest RMSE and MAE, and the highest d and NSE in testing and training processes.

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