TY - GEN
T1 - Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoring
AU - Daw, Arka
AU - Yeo, Kyongmin
AU - Karpatne, Anuj
AU - Klein, Levente
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Inferring the source information of greenhouse gases, such as methane, from spatially sparse sensor observations is an essential element in mitigating climate change. While it is well understood that the complex behavior of the atmospheric dispersion of such pollutants is governed by the Advection-Diffusion equation, it is difficult to directly apply the governing equations to identify the source location and magnitude (inverse problem) because of the spatially sparse and noisy observations, i.e., the pollution concentration is known only at the sensor locations and sensors sensitivity is limited. Here, we develop a multi-task learning framework that can provide high-fidelity reconstruction of the concentration field and identify emission characteristics of the pollution sources such as their location, emission strength, etc. from sparse sensor observations. We demonstrate that our proposed framework is able to achieve accurate reconstruction of the methane concentrations from sparse sensor measurements as well as precisely pin-point the location and emission strength of these pollution sources.
AB - Inferring the source information of greenhouse gases, such as methane, from spatially sparse sensor observations is an essential element in mitigating climate change. While it is well understood that the complex behavior of the atmospheric dispersion of such pollutants is governed by the Advection-Diffusion equation, it is difficult to directly apply the governing equations to identify the source location and magnitude (inverse problem) because of the spatially sparse and noisy observations, i.e., the pollution concentration is known only at the sensor locations and sensors sensitivity is limited. Here, we develop a multi-task learning framework that can provide high-fidelity reconstruction of the concentration field and identify emission characteristics of the pollution sources such as their location, emission strength, etc. from sparse sensor observations. We demonstrate that our proposed framework is able to achieve accurate reconstruction of the methane concentrations from sparse sensor measurements as well as precisely pin-point the location and emission strength of these pollution sources.
KW - Climate and Sustainability
KW - Physics-informed Machine Learning
KW - Uncertainty Quantification
UR - http://www.scopus.com/inward/record.url?scp=85147939788&partnerID=8YFLogxK
U2 - 10.1109/BigData55660.2022.10020603
DO - 10.1109/BigData55660.2022.10020603
M3 - Conference contribution
AN - SCOPUS:85147939788
T3 - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
SP - 4835
EP - 4841
BT - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
A2 - Tsumoto, Shusaku
A2 - Ohsawa, Yukio
A2 - Chen, Lei
A2 - Van den Poel, Dirk
A2 - Hu, Xiaohua
A2 - Motomura, Yoichi
A2 - Takagi, Takuya
A2 - Wu, Lingfei
A2 - Xie, Ying
A2 - Abe, Akihiro
A2 - Raghavan, Vijay
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Big Data, Big Data 2022
Y2 - 17 December 2022 through 20 December 2022
ER -