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Method to Improve Airborne Pollution Forecasting by Using Ant Colony Optimization and Neuro-Fuzzy Algorithms

International Journal of Intelligence Science 2014年第4期4卷 81-90页

作者： Elizabeth Martinez-Zeron Marco A. Aceves-Fernandez Efren Gorrostieta-Hurtado artemio sotomayor-olmedo Juan Manuel Ramos-Arreguín Facultad de Informática Universidad Autónoma de Querétaro Querétaro México Laboratory of Artificial Intelligence Faculty of Engineering Universidad Autónoma de Querétaro Querétaro México

This contribution shows the feasibility of improving the modeling of the non-linear behavior of airborne pollution in large cities. In previous works, models have been constructed using many machine learning algorithms. However, many of them do not work for all the pollutants, or are not consistent or robust for all cities. In this paper, an improved algorithm is proposed using Ant Colony Optimization (ACO) employing models created by a neuro-fuzzy system. This method results in a reduction of prediction error, which results in a more reliable prediction models obtained.

关键词： Neuro-Fuzzy models Ant Colony Optimization Airborne Pollution

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Analysis of Key Features of Non-Linear Behavior Using Recurrence Plots. Case Study: Urban Pollution at Mexico City

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Journal of Environmental Protection 2012年第9期3卷 1147-1160页

作者： Marco A. Aceves-Fernandez Jesus Carlos Pedraza-Ortega artemio sotomayor-olmedo Juan M. Ramos-Arreguín Jose Emilio Vargas-Soto Saul Tovar-Arriaga Campus Juriquilla Universidad Autonoma de Queretaro Santiago de Querétaro México

The use of Recurrence plots have been extensively used in various fields. In this work, Recurrence Plots (RPs) investigates the changes in the non-linear behaviour of urban air pollution using large datasets of raw data (hourly). This analysis has not been used before to extract information from large datasets for this type non-linear problem. Two different approaches have been used to tackle this problem. The first approach is to show results according to monitoring network. The second approach is to show the results by particle type. This analysis shows the feasibility of using Recurrence Analysis for pollution monitoring and control.

关键词： Recurrence Plot Air Quality Air Pollution Modelling Atmospheric Pollution Recurrence Quantification Analysis

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Forecast Urban Air Pollution in Mexico City by Using Support Vector Machines: A Kernel Performance Approach

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International Journal of Intelligence Science 2013年第3期3卷 126-135页

作者： artemio sotomayor-olmedo Marco A. Aceves-Fernández Efrén Gorrostieta-Hurtado Carlos Pedraza-Ortega Juan M. Ramos-Arreguín J. Emilio Vargas-Soto Universidad Autónoma de Querétaro Cerro de las Campanas S/N Querétaro México

The development of forecasting models for pollution particles shows a nonlinear dynamic behavior;hence, implementation is a non-trivial process. In the literature, there have been multiple models of particulate pollutants, which use softcomputing techniques and machine learning such as: multilayer perceptrons, neural networks, support vector machines, kernel algorithms, and so on. This paper presents a prediction pollution model using support vector machines and kernel functions, which are: Gaussian, Polynomial and Spline. Finally, the prediction results of ozone (O3), particulate matter (PM10) and nitrogen dioxide (NO2) at Mexico City are presented as a case study using these techniques.

关键词： Predictive Models Airborne Pollution Support Vector Machines Kernel Functions

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