The Neural Network Modeling of Suspended Particulate Matter with Autoregressive Structure


Ekoloji, 2010, Issue 74, Pages: 32-37


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Pollution sources and emissions, and their interactions with terrain and the atmosphere are necessary in developing appropriate air pollution management plans and action strategies. In this study, we will investigate the relationship between the total suspended particulate matter (TSP) concentration and meteorological parameters such as wind speed and direction, temperature, air pressure, precipitation and relative humidity. The TSP measurements o f the past two days, sunshine duration, and sunshine amount in the winter season (November through March) in the city o f Erzurum between the years 1990 and 2007 were investigated. The artificial neural network (ANN) models were constructed using a mixed autoregressive relationship to realize the stochastic nature o f the TSP levels for each month o f the winter season. The impact o f wind direction on TSP concentration was introduced to the model by defining two variables. Linear regression models were also constructed to check the performance o f the neural networks. The most significant factors affecting the TSP concentration are found to be the TSP level o f the previous day (TSP(_i), expected temperature (temp), wind speed (w), air pressure (p), and precipitation (pc).


Autoregressive, Erzurum, meteorological parameters, neural network, particulate matter


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