Forecasting PM 10 in the Bay of Algeciras Based on Regression Models

José Carlos Palomares-Salas, Juan José González- de-la-Rosa, Agustín Agüera-Pérez, José María Sierra-Fernández and Olivia Florencias-Oliveros
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José Carlos Palomares-Salas: Research Group PAIDI-TIC-168, Computational Instrumentation and Industrial Electronics (ICEI), Area of Electronics, University of Cádiz, Higher Polytechnic School, Av. Ramón Puyol S/N, E-11202 Algeciras, Spain
Juan José González- de-la-Rosa: Research Group PAIDI-TIC-168, Computational Instrumentation and Industrial Electronics (ICEI), Area of Electronics, University of Cádiz, Higher Polytechnic School, Av. Ramón Puyol S/N, E-11202 Algeciras, Spain
Agustín Agüera-Pérez: Research Group PAIDI-TIC-168, Computational Instrumentation and Industrial Electronics (ICEI), Area of Electronics, University of Cádiz, Higher Polytechnic School, Av. Ramón Puyol S/N, E-11202 Algeciras, Spain
José María Sierra-Fernández: Research Group PAIDI-TIC-168, Computational Instrumentation and Industrial Electronics (ICEI), Area of Electronics, University of Cádiz, Higher Polytechnic School, Av. Ramón Puyol S/N, E-11202 Algeciras, Spain
Olivia Florencias-Oliveros: Research Group PAIDI-TIC-168, Computational Instrumentation and Industrial Electronics (ICEI), Area of Electronics, University of Cádiz, Higher Polytechnic School, Av. Ramón Puyol S/N, E-11202 Algeciras, Spain

Sustainability, 2019, vol. 11, issue 4, 1-13

Abstract: Different forecasting methodologies, classified into parametric and nonparametric, were studied in order to predict the average concentration of P M 10 over the course of 24 h. The comparison of the forecasting models was based on four quality indexes (Pearson’s correlation coefficient, the index of agreement, the mean absolute error, and the root mean squared error). The proposed experimental procedure was put into practice in three urban centers belonging to the Bay of Algeciras (Andalusia, Spain). The prediction results obtained with the proposed models exceed those obtained with the reference models through the introduction of low-quality measurements as exogenous information. This proves that it is possible to improve performance by using additional information from the existing nonlinear relationships between the concentration of the pollutants and the meteorological variables.

Keywords: time-series forecasting; regression models; artificial neural networks; on-site measurements; exogenous information (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
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