Abstract
The monitoring and microclimate controlling in urbanized areas become one of the research hotspots in the field of air quality, where the application of Wireless Sensor Networks (WSN) recently attracts more attention due to its features of self-adaption, resilience, and cost-effectiveness. Present microclimate monitoring and control systems achieve their prediction by manipulating captured environmental factors and traditional neural network algorithms. However, these systems have a problem to solve the challenges of the quick prediction (e.g. hourly and even minutely) when the WSN network is deployed. In this paper, a novel prediction method based on a combination of Extended Kalman Filter (EKF) and an Extreme Learning Machine (ELM) algorithm is proposed to predict the key microclimate parameters like temperature, humidity, and barometric pressure level in a city urbanized areas. The outdoor air temperature, humidity, and barometric pressure in the air are measured as data samples via WSN based clusters managed by custom design operator stations. The results of the realized model simulation show that the processing speed rate of the proposed prediction model is significantly higher than other ANN-based models at a relatively good level of precision.
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This paper is supported by the National Scientific Program “Information and Communication Technologies for a Single Digital Market in Science, Education and Security (ICTinSES)”, financed by the Ministry of Education and Science.
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Alexandrov, A., Andreev, R., Ilchev, S., Boneva, A., Ivanov, S., Doshev, J. (2021). WSN-Based Prediction Model of Microclimate in a City Urbanized Areas Based on Extreme Learning and Kalman Filter. In: Dimov, I., Fidanova, S. (eds) Advances in High Performance Computing. HPC 2019. Studies in Computational Intelligence, vol 902. Springer, Cham. https://doi.org/10.1007/978-3-030-55347-0_2
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