Source Attribution and Emissions Quantification for Methane Leak Detection: A Non-linear Bayesian Regression Approach-Reference-Cited by-同舟云学术

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Source Attribution and Emissions Quantification for Methane Leak Detection: A Non-linear Bayesian Regression Approach

Published:2023 Issue: Volume: Page:279-293
ISSN:0302-9743
Container-title:Machine Learning, Optimization, and Data Science
language:
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Author:

Milletarì Mirco,Malvar Sara,Oruganti Yagna D.,Nunes Leonardo O.,Alaudah Yazeed,Badam Anirudh

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-25599-1_21

Reference25 articles.

1. NOAA ISD datasets. https://www.ncei.noaa.gov/products/land-based-station/automated-surface-weather-observing-systems/

2. Sentinel-2 imagery. https://sentinel.esa.int/web/sentinel/missions/sentinel-2?msclkid=7c80fe6cc7ba11ec9c5499250f796bd7

3. Texas Railroad Commission datasets. https://www.rrc.texas.gov/resource-center/research/data-sets-available-for-download/. Accessed 13 Sept 2021

4. Bingham, E., et al.: Pyro: deep universal probabilistic programming. J. Mach. Learn. Res. 20(1), 973–978 (2019)

5. Bowerman, B.L., O’Connell, R.T., Koehler, A.B.: Forecasting, Time Series and Regression: An Applied Approach. South-Western Pub, Southampton (2005)

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