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

Abstract

This article describes the application of a cellular neural network (CNN) to model air pollutants. In this study, forthcoming daily and hourly values of particulate matter (PM 10 ) and sulphur dioxide (SO 2 ) were predicted. These air pollutant concentrations were measured at four different locations (Yenibosna, Sarachane, Umraniye and Kadikoy) in Istanbul between 2002 and 2003. Eight different meteorological parameters (temperature, wind speed and direction, humidity, pressure, sunshine, cloudiness, rainfall) recorded at Florya and Goztepe meteorological stations were used to model inputs. First, the results of CNN prediction and statistical persistence method (PER) were compared. Then, CNN and PER outputs were correlated with real time values by using statistical performance indices. The indices of agreement (d) for daily mean concentrations were found using CNN and PER prediction models: 0.71-0.80 and 0.71-0.78 for PM 10 , and 0.81-0.84 and 0.77-0.82 for SO 2 in all air quality measurement stations, respectively. From these values, CNN prediction model are concluded to be more accurate than PER, which is used for comparison. In hourly prediction of mean concentrations with CNN, d value is found as 0.78 and 0.92 for PM 10 and SO 2 , respectively. Thus, it was concluded that CNN-based approaches could be promising for air pollutant prediction.

Majority Voting Based Multi-Task Clustering of Air Quality Monitoring Network in Turkey

“…The bold notation in the Algorithms/Methods column shows the algorithm which performs the best among the others. According to the findings, most of the experiments are done using the measurements of PM 10 concentrations [8][9][10][11][12][13] and prediction of air pollutant amount is the main goal. In addition to pollution data, some of the studies also integrate meteorological data such as temperature, wind speed, wind direction, pressure and humidity into the problem domain [8,[11][12][13].…”

Estimation of PM10 concentrations in Turkey based on Bayesian maximum entropy

“…Calculated PM10 levels by the model underestimated the observations with an average of 10% at the sampling station. Şahin et al [20] proposed the cellular neural network (CNN) method in order to modeling air pollutants such as PM10 concentrations in İstanbul. Meteorological parameters were used for model inputs.…”