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Abstract
This work presents a method of forecasting the level of PM10 with the use of artificial neural networks. Current level of particulate matter and meteorological data was taken into account in the construction of the model (checked the correlation of each variable and the future level of PM10), and unidirectional networks were used to implement it due to their ease of learning. Then, the configuration of the network (built on the basis of the developed model) was established, defining the number of layers and neurons, as well as the activation function. 4 methods of propagation (Back Propagation, Resilient Propagation, Manhattan Propagation and Scaled Conjugate Gradient) were applied in the network learning process to select the best method. The obtained results were then compared with real values and the complete network configuration (minimizing the forecast error) was determined. After completion of the learning process, the developed network was used to forecast the particulate matter levels in Gdansk.
Authors (2)
-
Cezary Orłowski prof dr. hab. inż.
- Wyższa Szkoła Bankowa w Gdańsku Instytut Zarządzania i Finansów
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- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Title of issue:
- Information Systems Architecture and Technology strony 15 - 30
- Language:
- English
- Publication year:
- 2014
- Bibliographic description:
- Sarzyński A., Orłowski C.: A MODEL FOR FORECASTING PM10 LEVELS WITH THE USE OF ARTIFICIAL NEURAL NETWORKS// Information Systems Architecture and Technology/ Wrocław: Oficyna Wydawnicza Politechniki Wrocławskiej, 2014, s.15-30
- Verified by:
- Gdańsk University of Technology
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