Hybrid Deep Learning and Sensitivity Operator-Based Algorithm for Identification of Localized Emission Sources

Alexey Penenko (), Mikhail Emelyanov, Evgeny Rusin, Erjena Tsybenova and Vasily Shablyko
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Alexey Penenko: Institute of Computational Mathematics and Mathematical Geophysics SB RAS, pr. Akademika Lavrentjeva 6, 630090 Novosibirsk, Russia
Mikhail Emelyanov: Institute of Computational Mathematics and Mathematical Geophysics SB RAS, pr. Akademika Lavrentjeva 6, 630090 Novosibirsk, Russia
Evgeny Rusin: Institute of Computational Mathematics and Mathematical Geophysics SB RAS, pr. Akademika Lavrentjeva 6, 630090 Novosibirsk, Russia
Erjena Tsybenova: Institute of Computational Mathematics and Mathematical Geophysics SB RAS, pr. Akademika Lavrentjeva 6, 630090 Novosibirsk, Russia
Vasily Shablyko: Institute of Computational Mathematics and Mathematical Geophysics SB RAS, pr. Akademika Lavrentjeva 6, 630090 Novosibirsk, Russia

Mathematics, 2023, vol. 12, issue 1, 1-21

Abstract: Hybrid approaches combining machine learning with traditional inverse problem solution methods represent a promising direction for the further development of inverse modeling algorithms. The paper proposes an approach to emission source identification from measurement data for advection–diffusion–reaction models. The approach combines general-type source identification and post-processing refinement: first, emission source identification by measurement data is carried out by a sensitivity operator-based algorithm, and then refinement is done by incorporating a priori information about unknown sources. A general-type distributed emission source identified at the first stage is transformed into a localized source consisting of multiple point-wise sources. The second, refinement stage consists of two steps: point-wise source localization and emission rate estimation. Emission source localization is carried out using deep learning with convolutional neural networks. Training samples are generated using a sensitivity operator obtained at the source identification stage. The algorithm was tested in regional remote sensing emission source identification scenarios for the Lake Baikal region and was able to refine the emission source reconstruction results. Hence, the aggregates used in traditional inverse problem solution algorithms can be successfully applied within machine learning frameworks to produce hybrid algorithms.

Keywords: inverse modeling; emission sources; air quality; source identification; sensitivity operator; deep learning; neural network; post-processing; remote sensing; localized sources (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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