TY - JOUR

T1 - Hybrid Deep Learning and Sensitivity Operator-Based Algorithm for Identification of Localized Emission Sources

AU - Penenko, Alexey

AU - Emelyanov, Mikhail

AU - Rusin, Evgeny

AU - Tsybenova, Erjena

AU - Shablyko, Vasily

N1 - The work was supported by grant 075-15-2020-787 in the form of a subsidy for a Major Scientific Project from the Ministry of Science and Higher Education of Russia (project “Fundamentals, methods and technologies for digital monitoring and forecasting of the environmental situation on the Baikal Natural Territory”).

PY - 2024/1

Y1 - 2024/1

N2 - 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.

AB - 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.

KW - air quality

KW - deep learning

KW - emission sources

KW - inverse modeling

KW - localized sources

KW - neural network

KW - post-processing

KW - remote sensing

KW - sensitivity operator

KW - source identification

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85182218913&origin=inward&txGid=b2f5d48056c8ad9e695ad62de3ade46b

UR - https://www.mendeley.com/catalogue/9276d994-cd69-3a66-b3ba-bc8b326af83b/

U2 - 10.3390/math12010078

DO - 10.3390/math12010078

M3 - Article

VL - 12

JO - Mathematics

JF - Mathematics

SN - 2227-7390

IS - 1

M1 - 78

ER -