Environmental odour management by artificial neural network – A review

Unwanted odour emissions are considered air pollutants that may cause detrimental impacts to the environment as well as an indicator of unhealthy air to the affected individuals resulting in annoyance and health related issues. These pollutants are challenging to handle due to their invisibility to the naked eye and can only be felt by the human olfactory stimuli. A strategy to address this issue is by introducing an intelligent processing system to odour monitoring instrument such as artificial neural network to achieve a robust result. In this paper, a review on the application of artificial neural network for the management of environmental odours is presented. The principal factors in developing an optimum artificial neural network were identified as elements, structure and learning algorithms. The management of environmental odour has been distinguished into four aspects such as measurement, characterization, control and treatment and continuous monitoring. For each aspect, the performance of the neural network is critically evaluated emphasizing the strengths and weaknesses. This work aims to address the scarcity of information by addressing the gaps from existing studies in terms of the selection of the most suitable configuration, the benefits and consequences. Adopting this technique could provide a new avenue in the management of environmental odours through the use of a powerful mathematical computing tool for a more efficient and reliable outcome. Keywords: Electronic nose, Environmental pollution, Human health, Odour emission, Public concer

Seismic Event Classification using Machine Learning

The manual detection of seismic events is a labor intensive task, requiring highly skilled workers continuously analyzing recorded waveforms. Previous work has shown the potential of machine learning methods for aiding in this task, and that deep neural networks are able to learn important patterns in seismic recordings. This study aims to develop a deep neural network to classify earthquake-, explosion and noise events using long beamformed waveform snippets from NORSAR's ARCES array. The final model was evaluated using an unseen test set and on recordings of the North Korean nuclear weapons tests. I developed custom augmentation methods in order to combat the uneven class distribution, and several preprocessing techniques were deployed in pursuit of performance. Models developed for similar data, state-of-the-art multivariate time series models, as well as self-developed models were experimented with and evaluated. Analysis of the results demonstrated that the final model can classify noise and explosion events with a high degree of accuracy, while earthquake classifications were less reliable. I conclude that deep neural networks can learn distinguishing features and detect events of interest on long beamformed three-component waveforms.Masteroppgave i informatikkINF399MAMN-INFMAMN-PRO