Air Pollution Data Modelling
The Brain-Like Artificial Intelligence (BLAI) is pioneered by Prof.Nikola Kasabov and here it is applied to an air quality case study.
This project develops novel methods and systems for modelling of multisensory streaming data in a real time for pollution estimation and for the prediction of the effect of it.
Using spiking neural networks and the NeuCube architecture to model multisensory streaming data and a case study on ozone concentration data modelling. Spiking neural networks can describe the spatial and temporal relationships among the variables that describe the dynamics of a system. Particularly, we use the NeuCube architecture to model and study the concentrations of greenhouse gases such as carbon monoxide (CO) and nitrogen dioxide (NO2) at a time, and their relationship with nocturnal spikes in ozone (O3) concentrations. Spatial and temporal patterns associated with high ozone concentrations are related to increased hospital admissions. NeuCube can work with high resolution data which results in a more effective model. Understanding the system better leads to evaluating the temporal and spatial occurrence on nocturnal spikes in ozone concentrations. This allows us to have better predictions to establish the health impact and its outcomes. In general, a better streaming data modelling of multisensory data for a better classification of events, better prediction and a better understanding of the processes measured by the sensors. In our case study: NeuCube is used to model relationship between ozone concentrations and simultaneous increments of temperature, periods of elevated nitrogen monoxide concentrations and ozone episodes. We can also determine the reasons why nocturnal spikes are most likely to be found in certain areas. Nowadays, nocturnal spikes in ozone concentration are unrelated to health concerns. However, NeuCube can incorporate diverse variables and can also model their relationships, in order to quantify not only health costs of air pollution but also the economic effect of emission changes.
Figure. NeuCube 3D spiking neural network map of southwestern British Columbia showing the Lower Fraser Valley network of monitors with regional and government fixed monitors (dark green circles). Spatio-temporal relationships (lines) and activity (light green circles) of ozone (O3) (left cube) and carbon monoxide (CO) (right cube) concentrations can be analysed simultaneously.
Related Papers and Benchmarking
The proposed methods and systems, when compared with traditional statistical and machine learning methods, showed superior results in the following aspects:
- NeuCube can help us to model the relationship between nocturnal spikes in ozone concentrations and simultaneous increments of temperature, periods of elevated nitrogen monoxide concentrations and ozone episodes;
- The 3D and Virtual Reality visualisation eases to study why nocturnal spikes in ozone concentration are most likely found in certain areasp;
- Nowadays, nocturnal spikes in ozone concentration are unrelated to health concerns. However, NeuCube can incorporate diverse variables and model their relationships, in order to quantify not only health costs of air pollution but also the economic effect of emission changes.
See also some of the related papers:
Salmond, J., & McKendry, I. G. (2002). Secondary ozone maxima in a very stable nocturnal boundary layer: observations from the Lower Fraser Valley, BC. Atmospheric Environment, 36(38), 5771-5782.
Bart, M., Williams, D. E., Ainslie, B., McKendry, I., Salmond, J., Grange, S. K., Alavi-Shoshtari, M., Steyn, D., & Henshaw, G. S. (2014). High density ozone monitoring using gas sensitive semi-conductor sensors in the Lower Fraser Valley, British Columbia. Environmental science & technology, 48(7), 3970-3977.
Agudelo–Castaneda, D. M., Teixeira, E. C., & Pereira, F. N. (2014). Time–series analysis of surface ozone and nitrogen oxides concentrations in an urban area at Brazil. Atmospheric Pollution Research, 5(3), 411-420.
Kasabov, N. K. (2014). NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data. Neural Networks, 52, 62-76.
Kasabov, N., Scott, N. M., Tu, E., Marks, S., Sengupta, N., Capecci, E., Othman, M., Gholoami Doborjeh, M., Murli, N., Hartono, R., Espinosa-Ramos, J. I., Zhou, L., Alvi, F., Wang, G., Taylor, D., Feigin, V., Gulyaev, S., Mahmoud, M., Hou, Z. G., Yang, J. (2016). Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: design methodology and selected applications. Neural Networks, 78, 1-14.
For this project, an R&D system has been developed based on NeuCube. The system can be obtained subject to licensing agreement.
The developer of this project is: