PREDICTION AND MODELING OF CARBON MONOXIDE CONCENTRATION WITH THE COMBINATION OF AN ADAPTIVE NEURO-FUZZY NETWORK AND GIS

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KHAZAEI E.; ALE SHEYKH A.A.; KARIMI M.; VAHIDNIA H.

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Journal: Journal of RS and GIS for Natural Resources Year:2012 | Volume:3 | Issue:3 Page(s): 21-35

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Information Journal Paper

Title

PREDICTION AND MODELING OF CARBON MONOXIDE CONCENTRATION WITH THE COMBINATION OF AN ADAPTIVE NEURO-FUZZY NETWORK AND GIS

Author(s)

KHAZAEI E. | ALE SHEYKH A.A. | KARIMI M. | VAHIDNIA H. | Issue Writer Certificate

Keywords

AIR POLLUTIONQ1

ADAPTIVE NEURO-FUZZY NETWORKQ2

FUZZY SYSTEMQ2

KRIGINGQ2

GISQ2

Abstract

Nowadays, AIR POLLUTION is the main environmental challenge in metropolises. Therefore, it is essential to monitor and forecast air quality parameters in urban areas. It depends upon various factors, including topography, climate, population and transportation network. The relationship between these special factors has been considered as a dynamic, the nonlinear and ambiguous phenomenon. In this study, an adaptive Neuro –fuzzy system and GIS have been used to extract knowledge of environment from data, in terms of fuzzy rules. These rules were used to predict and model carbon monoxide (CO) pollutant concentration. Tehran has been selected as the case study. The data gathered from six meteorological stations, for four consecutive years in summer, in this city were used separately to train the neural network. Fuzzy rules (Sugeno and Mamdani) were extracted for each station and then, using these rules; pollutant concentration was estimated. Having concentration predictions at station points, log- KRIGING was used to model the spatial concentration in the area selected as the case study. The results showed that average RMSE of all stations using Sugeno rules is 1.445 and using Mamdani rules is 1.374.

Cites

Spatial and temporal variations of air pollutants from motor vehicles using MOVES software: case study of Beheshti and Modarres boulevards in Rasht

References

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APA: Copy

KHAZAEI, E., ALE SHEYKH, A.A., KARIMI, M., & VAHIDNIA, H.. (2012). PREDICTION AND MODELING OF CARBON MONOXIDE CONCENTRATION WITH THE COMBINATION OF AN ADAPTIVE NEURO-FUZZY NETWORK AND GIS. JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE), 3(3), 21-35. SID. https://sid.ir/paper/189421/en

Vancouver: Copy

KHAZAEI E., ALE SHEYKH A.A., KARIMI M., VAHIDNIA H.. PREDICTION AND MODELING OF CARBON MONOXIDE CONCENTRATION WITH THE COMBINATION OF AN ADAPTIVE NEURO-FUZZY NETWORK AND GIS. JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE)[Internet]. 2012;3(3):21-35. Available from: https://sid.ir/paper/189421/en

IEEE: Copy

E. KHAZAEI, A.A. ALE SHEYKH, M. KARIMI, and H. VAHIDNIA, “PREDICTION AND MODELING OF CARBON MONOXIDE CONCENTRATION WITH THE COMBINATION OF AN ADAPTIVE NEURO-FUZZY NETWORK AND GIS,” JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE), vol. 3, no. 3, pp. 21–35, 2012, [Online]. Available: https://sid.ir/paper/189421/en

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