Time Series Forecasting Of Hourly PM10 Values:
Model Intercomparison And
The Development Of Localized Linear Approaches
Author(s)
D. Vlachogiannis & A. Sfetsos
Abstract
This paper presents a comparison of various point modelling approaches, using
time series analysis, on the prediction of hourly PM10 values from Helsinki,
Finland. In addition to the conventional linear models and the commonly used
feed forward neural networks (FFNN or MLP), hybrid modelling tools based on
clustering and local linear models are introduced. The developed models are
evaluated for their ability to produce accurate forecasts. The hybrid clustering
algorithm returned the overall best forecasting ability, reducing prediction error
from all conventional approaches by more than 15%.
Keywords: time series models, neural networks, hybrid models.
1 Introduction
Environmental health research demonstrated that particulate matter is a top
priority pollutant for public health Studies of long-term exposure to air pollution,
especially to particulate matter (PM), suggest an increased mortality e.g. [1],
increased risk of cardiovascular related diseases [2], and of developing various
types of cancer [3].
In order to evaluate the ambient air concentrations of particulate matter, a
deterministic urban air quality model should include the modelling of turbulent
diffusion, deposition, resuspension, chemical reactions and aerosol processes. In
recent years an emerging trend is the application of machine learning algorithms
(MLA), and especially Artificial Neural Networks (ANN) in PM modelling, as a
means to derive both spatial and spatio-temporal predictions a from observations
in a location of interest. The strength of these methodologies lies in their ability
Keywords
time series models, neural networks, hybrid models.
Related Book
Other papers in this volume
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