Modeling of SO2 distribution in Istanbul using artificial neural networks

Abstract

Urban air pollution is a growing problem in developing countries. Some compounds especially sulphur dioxide (SO 2 ) is considered as typical indicators of the urban air quality. Air pollution modeling and prediction have great importance in preventing the occurrence of air pollution episodes and provide sufficient time to take the necessary precautions. Recently, various stochastic image-processing algorithms such as Artificial Neural Network (ANN) are applied to environmental engineering. ANN structure employs input, hidden and output layers. Due to the complexity of the problem, as the number of input-output parameters differs, ANN model settings such as the number of neurons of these layers changes. The ability of ANN models to learn, particularly capability of handling large amounts (or sets) of data simultaneously as well as their fast response time, are invariably the characteristics desired for predictive and forecasting purposes. In this paper, ANN models have been used to predict air pollutant parameter in meteorological considerations. We have especially focused on modeling of SO 2 distribution and predicting its future concentration in Istanbul, Turkey. We have obtained data sets including meteorological variables and SO 2 concentrations from Istanbul-Florya meteorological station and Istanbul-Yenibosna air pollution station. We have preferred three-layer perceptron type of ANN which consists of 10, 22 and 1 neurons for input, hidden and output layers, respectively. All considered parameters are measured as daily mean. The input parameters are: SO 2 concentration, pressure, temperature, humidity, wind direction, wind speed, strength of sunshine, sunshine, cloudy, rainfall and output parameter is the future prediction of SO 2 . To evaluate the performance of ANN model, our results are compared to classical nonlinear regression methods. The over all system finds an optimum correlation between input-output variables. Here, the correlation parameter, r is 0.999 and 0.528 for training and test data. Thus in our model, the trend of SO 2 is well estimated and seasonal effects are well represented. As a result, we conclude that ANN is one of the compromising methods in estimation of environmental complex air pollution problems.

Urban Air Pollution Forecasting Using Artificial Intelligence-Based Tools

“…Ulku Sahin et al (Ulku Sahin et al, 2005) and Viotti et al (Viotti et al, 2002) introduced the ANN-based tools. In Ulku Sahin et al's paper, they evaluated the performance by using ANN model or results to compare to other classical nonlinear methods.…”

“…As one of the most compromising AI methods in estimation of environmental complex air pollution problems, ANN has been used by many scientists, such as (Ulku Sahin et al, 2005) and (P. Viotti et al, 2002). In the study of Ulku Sahin et al, ANN approach was used for predicting SO 2 concentration in Bahcelievler region.…”

“…In the study of Ulku Sahin et al, ANN approach was used for predicting SO 2 concentration in Bahcelievler region. In this paper, the results were used to compare to nonlinear regression for actual measured values (Ulku Sahin et al, 2005). By comparing maximum and minimum values of observed SO 2 which were predicted by ANN model and nonlinear regression respectively, the results which are from ANN are quite realistic.…”

A New Approach to Prediction of SO2and PM10Concentrations in Istanbul, Turkey: Cellular Neural Network (CNN)

“…This approach is concerned with the design and development of algorithms that allow computers to empirically learn the behavior of data sets. In many studies, ANNs are applied to predict environmental pollutants (Boznar et al, 1993;Mok and Tam, 1998;Saral and Ertürk, 2003;Chelani et al, 2002;Sahin et al, 2005;Raha, 2007). Gardner and Dorling (1998) published a comprehensive review of studies using an ANN approach for environmental air pollution modeling.…”

“…Kukkonen et al (2003) studied five neural network (NN) models, a linear statistical model and a deterministic modeling system for the prediction of urban NO 2 and PM 10 concentrations. Sahin et al (2004) used a multi-layer neural network model to predict daily CO concentrations in the European side of Istanbul, Turkey, by using meteorological variables. Kurt et al (2008) also developed an online air pollution forecasting system for Istanbul using NN.…”