Predicting Global Marine Sediment Density Using the Random Forest Regressor Machine Learning Algorithm

Graw, Jordan H.; Wood, W. T.; Phrampus, B. J.

doi:10.1029/2020jb020135

Cited by 45 publications

(26 citation statements)

References 20 publications

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“… 2020b ) 0.5 0.54 22.7 0.02 101.01 0.2 0.27 17.3 0.01 35.17 0.69 0.72 3.48 0.02 4.21 PoissonRegressor (Ranzato and Szummer 2008 ) 0.5 0.55 22.64 0.02 73.94 0.47 0.51 14.14 0.02 45.12 0.71 0.74 3.38 0.02 8.32 RandomForestRegressor (Graw et al. 2021 ) 0.47 0.52 23.36 0.19 74.12 0.49 0.53 13.86 0.18 45.12 0.8 0.82 2.82 0.18 8.28 RANSACRegressor (Aziz et al. 2020 ) 0.46 0.51 23.51 0.08 71.43 0.41 0.47 14.8 0.08 40.97 0.75 0.78 3.11 0.07 …”

Section: Discussionunclassified

Machine learning analysis on the impacts of COVID-19 on India’s renewable energy transitions and air quality

Stephan

Al‐Turjman²,

Wang

et al. 2022

Environ Sci Pollut Res

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India is severely affected by the COVID-19 pandemic and is facing an unprecedented public health emergency. While the country’s immediate measures focus on combating the coronavirus spread, it is important to investigate the impacts of the current crisis on India’s renewable energy transition and air quality. India’s economic slowdown is mainly compounded by the collapse of global oil prices and the erosion of global energy demand. A clean energy transition is a key step in enabling the integration of energy and climate. Millions in India are affected owing to fossil fuel pollution and the increasing climate heating that has led to inconceivable health impacts. This paper attempts to study the impact of COVID-19 on India’s climate and renewable energy transitions through machine learning algorithms. India is observing a massive collapse in energy demand during the lockdown as its coal generation is suffering the worst part of the ongoing pandemic. During this current COVID-19 crisis, the renewable energy sector benefits from its competitive cost and the Indian government’s must-run status to run generators based on renewable energy sources. In contrast to fossil fuel-based power plants, renewable energy sources are not exposed to the same supply chain disruptions in this current pandemic situation. India has the definite potential to surprise the global community and contribute to cost-effective decarbonization. Moreover, the country has a good chance of building more flexibility into the renewable energy sector to avoid an unstable future.

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Section: Discussionunclassified

Machine learning analysis on the impacts of COVID-19 on India’s renewable energy transitions and air quality

Stephan

Al‐Turjman²,

Wang

et al. 2022

Environ Sci Pollut Res

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“…Granat et al (2021) utilize clustering methods on global navigation satellite system data to identify major faults in California. Graw et al (2021) utilize the random forest regressor to interpolate for a global marine sediment density map from measurements at a few sparsely distributed locations. You et al (2021) train a generative adversarial network to compress complex two-dimensional digital rock images into one-dimensional latent space vectors and exploit the linearity of these vectors to interpolate between the two-dimensional images for a complete three-dimensional rock structure.…”

Section: Geophysical Data Processing and Image Interpretationmentioning

confidence: 99%

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

O’Malley

Beroza

et al. 2023

JGR Solid Earth

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After decades of low but continuing activity, applications of machine learning (ML) in solid Earth geoscience have exploded in popularity. This special collection provides a snapshot of those applications, which range from data processing to inversion and interpretation, for which ML appears particularly well suited. Inevitably, there are variations in the degree to which these methods have been developed. We hope that the progress seen in some areas will inspire efforts in others. Challenges remain, including the formidable task of how geoscience can keep pace with developments in ML while ensuring the scientific rigor that our field depends on, but with improvements in sensor technology and accelerating rates of data accumulation, the methods of ML seem poised to play an important role for the foreseeable future.

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“…Efforts for marine geoscientists to become "data literate" beyond the immediate needs of their own datasets are already underway Frontiers in Earth Science frontiersin.org through organizations such as Community Surface Dynamics Modeling System (CSDMS) and the Research Data Alliance (Berman et al, 2014). An example of data-driven marine geoscience can be found in recent machine learning efforts that provide both marine geoscience analyses and identify parametrically unique regions to sample (e.g., Lee et al, 2019;Graw et al, 2020). Analyses such as these pinpoint regions of geologic interest, instead of geographic interest, that are ideal for further data collection.…”

Section: Improvementsmentioning

confidence: 99%

The necessary optimization of the data lifecycle: Marine geosciences in the big data era

Lee

Phrampus²,

Obelcz³

2023

Front. Earth Sci.

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In the marine geosciences, observations are typically acquired using research vessels to understand a given phenomenon or area of interest. Despite the plateauing of ship time and active research vessels in the last decade, the rate of marine geoscience data production has continued to increase. Simultaneously, there exists large quantities of legacy data aggregated within data repositories; however, these data are rarely curated to be both discoverable and machine-readable (i.e., accessible). This results in inefficient use, or even omission, of high-quality data, that is, both increasingly important to utilize and impractical to recollect. The proliferation of newly acquired data, and increasing importance of legacy data, has only been met with incremental evolution in the methods of data integration. This paper describes some improvements at each stage of the data lifecycle (acquisition, curation, and integration) that could align the marine geosciences better with the “big data” paradigm. We have encountered several major issues coordinating these efforts which we outline here: 1) geologic anomalies are the primary focus of data acquisition and pose difficulty in understanding the dominant (i.e., baseline) marine geology, 2) marine geoscience data are rarely curated to be accessible, and 3) aforementioned issues preclude the use of efficient integration tools that can make optimal use of data. In this paper, we discuss challenges and solutions associated with these issues to overcome these concerns in future decades of marine geoscience. The successful execution of these interconnected steps will optimize the lifecycle of marine geoscience data in the “big data” era.

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Predicting Global Marine Sediment Density Using the Random Forest Regressor Machine Learning Algorithm

Abstract: One such study, on a global scale, evaluated several subsurface physical properties, and culminated in the CRUST1.0 Earth model (Laske et al., 2013). The CRUST1.0 Earth model is a 1°×1° global assessment of

Cited by 45 publications

References 20 publications

Machine learning analysis on the impacts of COVID-19 on India’s renewable energy transitions and air quality

Machine learning analysis on the impacts of COVID-19 on India’s renewable energy transitions and air quality

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

The necessary optimization of the data lifecycle: Marine geosciences in the big data era

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