Product Multi-kernels for Sensor Data Analysis

Vidnerová, Petra; Neruda, Roman

doi:10.1007/978-3-319-19324-3_12

Cited by 1 publication

(1 citation statement)

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“…In our previous papers [28,29], we have proposed composite kernel functions for RN and, consequently, evolutionary algorithms searching for data-dependent optimal kernel functions have been proposed in [30] for networks with product units, and in [31] for networks with sum units. Based on Aronszajn's breakthrough results [32], we have shown that it is possible to use composite kernels as activation functions in the RNs (cf.…”

Section: Composite Kernelsmentioning

confidence: 99%

Air Pollution Modelling by Machine Learning Methods

Vidnerová

Neruda

2021

Modelling

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Precise environmental modelling of pollutants distributions represents a key factor for addresing the issue of urban air pollution. Nowadays, urban air pollution monitoring is primarily carried out by employing sparse networks of spatially distributed fixed stations. The work in this paper aims at improving the situation by utilizing machine learning models to process the outputs of multi-sensor devices that are small, cheap, albeit less reliable, thus a massive urban deployment of those devices is possible. The main contribution of the paper is the design of a mathematical model providing sensor fusion to extract the information and transform it into the desired pollutant concentrations. Multi-sensor outputs are used as input information for a particular machine learning model trained to produce the CO, NO2, and NOx concentration estimates. Several state-of-the-art machine learning methods, including original algorithms proposed by the authors, are utilized in this study: kernel methods, regularization networks, regularization networks with composite kernels, and deep neural networks. All methods are augmented with a proper hyper-parameter search to achieve the optimal performance for each model. All the methods considered achieved vital results, deep neural networks exhibited the best generalization ability, and regularization networks with product kernels achieved the best fitting of the training set.

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