https://csdms.colorado.edu/csdms_wiki/api.php?action=feedcontributions&user=Jobelcz&feedformat=atomCSDMS - User contributions [en]2024-03-29T14:01:29ZUser contributionsMediaWiki 1.38.4https://csdms.colorado.edu/csdms_wiki/index.php?title=2019_CSDMS_meeting-117&diff=2243352019 CSDMS meeting-1172019-04-01T21:43:05Z<p>Jobelcz: Created page with "{{CSDMS meeting personal information template-2019 |CSDMS meeting first name=Jeffrey |CSDMS meeting last name=Obelcz |CSDMS meeting institute=Naval Research Lab |CSDMS meeting..."</p>
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<div>{{CSDMS meeting personal information template-2019<br />
|CSDMS meeting first name=Jeffrey<br />
|CSDMS meeting last name=Obelcz<br />
|CSDMS meeting institute=Naval Research Lab<br />
|CSDMS meeting city=Stennis Space Center<br />
|CSDMS meeting country=United States<br />
|CSDMS meeting state=Mississippi<br />
|CSDMS meeting email address=jbobelcz@gmail.com<br />
|CSDMS meeting phone=4845479143<br />
}}<br />
{{CSDMS meeting scholar and pre-meeting2019<br />
|CSDMS meeting pre-conference2019=None<br />
}}<br />
{{CSDMS meeting select clinics1 2019<br />
|CSDMS_meeting_select_clinics1_2019=3) Pangeo - Scalable Geoscience Tools Python<br />
}}<br />
{{CSDMS meeting select clinics2 2019<br />
|CSDMS_meeting_select_clinics2_2019=4) Model Calibration with Dakota<br />
}}<br />
{{CSDMS meeting select clinics3 2019<br />
|CSDMS_meeting_select_clinics3_2019=4) Making models - Data FAIR<br />
}}<br />
{{CSDMS scholarships yes no<br />
|CSDMS meeting scholarships=No<br />
}}<br />
{{CSDMS meeting abstract yes no 2019<br />
|CSDMS meeting abstract submit=Yes<br />
}}<br />
{{CSDMS meeting abstract title temp2019<br />
|CSDMS meeting abstract title=Geospatial prediction of subsurface wood-bearing sediments, Northern Gulf of Mexico<br />
}}<br />
{{CSDMS meeting authors template<br />
|CSDMS meeting coauthor first name abstract=Warren<br />
|CSDMS meeting coauthor last name abstract=Wood<br />
|CSDMS meeting coauthor institute / Organization=Naval Research Lab<br />
|CSDMS meeting coauthor town-city=Stennis Space Center<br />
|CSDMS meeting coauthor country=United States<br />
|State=Mississippi<br />
|CSDMS meeting coauthor email address=Warren.Wood@nrlssc.navy.mil<br />
}}<br />
{{CSDMS meeting authors template<br />
|CSDMS meeting coauthor first name abstract=Benjamin<br />
|CSDMS meeting coauthor last name abstract=Phrampus<br />
|CSDMS meeting coauthor institute / Organization=Naval Research Lab<br />
|CSDMS meeting coauthor town-city=Stennis Space Center<br />
|CSDMS meeting coauthor country=United States<br />
|State=Mississippi<br />
|CSDMS meeting coauthor email address=Benjamin.Phrampus.ctr@nrlssc.navy.mil<br />
}}<br />
{{CSDMS meeting abstract template 2019<br />
|CSDMS meeting abstract=Recent discovery of a well-preserved drowned bald cypress forest offshore Alabama has spurred the search for analogous sites, as they provide valuable paleoclimate proxies and potential paleohuman habitats. However, drowned forests are difficult to detect when buried beneath the seabed, and degrade rapidly when exposed to the water column. <br />
<br />
In this study, various machine learning algorithms within NRL's Global Predictive Seabed Model (GPSM) are used to geospatially predict the location of buried ancient forests offshore Mississippi. Subsurface sediment cores containing evidence of ancient forests (wood debris) are used as training and validation data, and feature layers include modern bathymetry, paleo-topographic surfaces, and seabed substrate. The resulting maps of probability of encountering wood-bearing sediments will be used to guide future data acquisition efforts.<br />
}}<br />
{{blank line template}}</div>Jobelczhttps://csdms.colorado.edu/csdms_wiki/index.php?title=AIandML&diff=217573AIandML2018-04-30T21:06:37Z<p>Jobelcz: </p>
<hr />
<div>2018 CSDMS Annual Meeting:<br />
<br />
== '''With Artificial Intelligence & Machine Learning - What Lies Ahead for Earth Surface Modeling ?''' ==<br />
<br />
A Forum, 1030-1230pm, 24th May 2018, SEEC Room ###<br />
Convened by Chris JENKINS (INSTAAR, Boulder CO) and Jeff OBELCZ (NRL, Stennis, MS)<br />
<br />
<br />
=== '''Questions for the Forum''' ===<br />
* What can AI and ML currently do that might benefit Earth Surface Dynamics Modeling ?<br />
* What is the relationship between Process Modelling and AI/ML ?<br />
* How should CSDMS Community Respond to the Appearance of AI/ML<br />
* What Earth Surface Dynamics Modeling-related tasks are not suited for AI/ML? Why?<br />
<br />
=== '''Background Reading''' ===<br />
* Participants can share URL's here to papers that discuss the workings of AI/ML and applications in fields such as ours. Some papers are also relevant to questions for the forum (above).<br />
<br />
Jones, N. 2018. [https://www.nature.com/news/how-machine-learning-could-help-to-improve-climate-forecasts-1.22503 How machine learning could help to improve climate forecasts]. Nature 548, 379–380 (24 August 2017) doi:10.1038/548379a .<br />
<br />
Grover, A. et al. 2015. [http://erichorvitz.com/weather_hybrid_representation.pdf A Deep Hybrid Model for Weather Forecasting]. 2015 ACM, DOI: http://dx.doi.org/10.1145/2783258.2783275.<br />
<br />
Abbot, J. & Marohasy,J. 2013. [https://www.witpress.com/elibrary/wit-transactions-on-ecology-and-the-environment/172/24639 The Application Of Artificial Intelligence For Monthly Rainfall Forecasting In The Brisbane Catchment, Queensland, Australia.] WIT Transactions on Ecology and the Environment, 172, 125 - 135. DOI:10.2495/RBM130111<br />
<br />
Karpatne, A., et al., 2017. [https://arxiv.org/pdf/1711.04708.pdf Machine Learning for the Geosciences: Challenges and Opportunities.] Workshop on Mining Big Data in Climate and Environment (MBDCE 2017), 17th SIAM International Conference on Data Mining (SDM 2017).<br />
<br />
Burghard, C. 2017. [http://www.nvidia.com/object/idc-deep-learning-in-healthcare.html From Bench to Bedside: Deep Learning’s Journey in Healthcare. ] (Registration required)<br />
<br />
Datascience 2018. [https://www.datascience.com/resources Resources.] Culver City CA (Commercially oriented source of up-to-date briefings, useful even down to technical levels.)<br />
<br />
Joppa, L.N. 2017. [https://www.nature.com/articles/d41586-017-08675-7 The case for technology investments in the environment. Create an artificial-intelligence platform for the planet, urges Lucas N. Joppa.] Nature 552, 325-328 (2017), doi:10.1038/d41586-017-08675-7.<br />
<br />
Marone, C. 2018. [https://www.nature.com/articles/s41561-018-0117-5?WT.ec_id=NGEO-201805&spMailingID=56516686&spUserID=MTg1NDc3MTE2MzY3S0&spJobID=1384567859&spReportId=MTM4NDU2Nzg1OQS2 Training machines in Earthly ways]. Nature Geoscience 11, 301-302 (2018), doi: 10.1038/s41561-018-0117-5 .<br />
<br />
=== '''Displays during the Forum''' ===<br />
* Posters, and printed materials for distribution will be available at the event<br />
<br />
=== '''Online Resources for the Forum''' ===<br />
* This Wiki will serve Abstracts, URL's, Posters, Images supplied by participants before and during the meeting<br />
<br />
<br />
<small>''Dateline: CJ 12Feb2018''</small><small>Small text</small></div>Jobelczhttps://csdms.colorado.edu/csdms_wiki/index.php?title=AIandML&diff=216903AIandML2018-02-20T18:34:19Z<p>Jobelcz: /* Background Reading */</p>
<hr />
<div>2018 CSDMS Annual Meeting:<br />
<br />
== '''With Artificial Intelligence & Machine Learning - What Lies Ahead for Earth Surface Modeling ?''' ==<br />
<br />
A Forum, 1030-1230pm, 24th May 2018, SEEC Room ###<br />
Convened by Chris JENKINS (INSTAAR, Boulder CO) and Jeff OBELCZ (NRL, Stennis, MS)<br />
<br />
<br />
=== '''Questions for the Forum''' ===<br />
* What can AI and ML currently do that might benefit Earth Surface Dynamics Modeling ?<br />
* What is the relationship between Process Modelling and AI/ML ?<br />
* How should CSDMS Community Respond to the Appearance of AI/ML<br />
* What Earth Surface Dynamics Modeling-related tasks are not suited for AI/ML? Why?<br />
<br />
=== '''Background Reading''' ===<br />
* Participants can share URL's here to papers that discuss the workings of AI/ML and applications in fields such as ours. Some papers are also relevant to questions for the forum (above).<br />
<br />
Jones, N. 2018. [https://www.nature.com/news/how-machine-learning-could-help-to-improve-climate-forecasts-1.22503 How machine learning could help to improve climate forecasts]. Nature 548, 379–380 (24 August 2017) doi:10.1038/548379a .<br />
<br />
Grover, A. et al. 2015. [http://erichorvitz.com/weather_hybrid_representation.pdf A Deep Hybrid Model for Weather Forecasting]. 2015 ACM, DOI: http://dx.doi.org/10.1145/2783258.2783275.<br />
<br />
Abbot, J. & Marohasy,J. 2013. [https://www.witpress.com/elibrary/wit-transactions-on-ecology-and-the-environment/172/24639 The Application Of Artificial Intelligence For Monthly Rainfall Forecasting In The Brisbane Catchment, Queensland, Australia.] WIT Transactions on Ecology and the Environment, 172, 125 - 135. DOI:10.2495/RBM130111<br />
<br />
Karpatne, A., et al., 2017. [https://arxiv.org/pdf/1711.04708.pdf Machine Learning for the Geosciences: Challenges and Opportunities.] Workshop on Mining Big Data in Climate and Environment (MBDCE 2017), 17th SIAM International Conference on Data Mining (SDM 2017).<br />
<br />
=== '''Displays during the Forum''' ===<br />
* Posters, and printed materials for distribution will be available at the event<br />
<br />
=== '''Online Resources for the Forum''' ===<br />
* This Wiki will serve Abstracts, URL's, Posters, Images supplied by participants before and during the meeting<br />
<br />
<br />
<small>''Dateline: CJ 12Feb2018''</small><small>Small text</small></div>Jobelczhttps://csdms.colorado.edu/csdms_wiki/index.php?title=AIandML&diff=216902AIandML2018-02-20T18:22:39Z<p>Jobelcz: /* Questions for the Forum */</p>
<hr />
<div>2018 CSDMS Annual Meeting:<br />
<br />
== '''With Artificial Intelligence & Machine Learning - What Lies Ahead for Earth Surface Modeling ?''' ==<br />
<br />
A Forum, 1030-1230pm, 24th May 2018, SEEC Room ###<br />
Convened by Chris JENKINS (INSTAAR, Boulder CO) and Jeff OBELCZ (NRL, Stennis, MS)<br />
<br />
<br />
=== '''Questions for the Forum''' ===<br />
* What can AI and ML currently do that might benefit Earth Surface Dynamics Modeling ?<br />
* What is the relationship between Process Modelling and AI/ML ?<br />
* How should CSDMS Community Respond to the Appearance of AI/ML<br />
* What Earth Surface Dynamics Modeling-related tasks are not suited for AI/ML? Why?<br />
<br />
=== '''Background Reading''' ===<br />
* Participants can share URL's here to papers that discuss the workings of AI/ML and applications in fields such as ours. Some papers are also relevant to questions for the forum (above).<br />
<br />
Jones, N. 2018. [https://www.nature.com/news/how-machine-learning-could-help-to-improve-climate-forecasts-1.22503 How machine learning could help to improve climate forecasts]. Nature 548, 379–380 (24 August 2017) doi:10.1038/548379a .<br />
<br />
Grover, A. et al. 2015. [http://erichorvitz.com/weather_hybrid_representation.pdf A Deep Hybrid Model for Weather Forecasting]. 2015 ACM, DOI: http://dx.doi.org/10.1145/2783258.2783275.<br />
<br />
Abbot, J. & Marohasy,J. 2013. [https://www.witpress.com/elibrary/wit-transactions-on-ecology-and-the-environment/172/24639 The Application Of Artificial Intelligence For Monthly Rainfall Forecasting In The Brisbane Catchment, Queensland, Australia.] WIT Transactions on Ecology and the Environment, 172, 125 - 135. DOI:10.2495/RBM130111<br />
<br />
=== '''Displays during the Forum''' ===<br />
* Posters, and printed materials for distribution will be available at the event<br />
<br />
=== '''Online Resources for the Forum''' ===<br />
* This Wiki will serve Abstracts, URL's, Posters, Images supplied by participants before and during the meeting<br />
<br />
<br />
<small>''Dateline: CJ 12Feb2018''</small><small>Small text</small></div>Jobelczhttps://csdms.colorado.edu/csdms_wiki/index.php?title=2018_CSDMS_meeting-012&diff=2153292018 CSDMS meeting-0122018-01-17T18:28:30Z<p>Jobelcz: Created page with "{{CSDMS meeting personal information template-2018 |CSDMS meeting first name=Jeffrey |CSDMS meeting last name=Obelcz |CSDMS meeting institute=Naval Research Lab |CSDMS meeting..."</p>
<hr />
<div>{{CSDMS meeting personal information template-2018<br />
|CSDMS meeting first name=Jeffrey<br />
|CSDMS meeting last name=Obelcz<br />
|CSDMS meeting institute=Naval Research Lab<br />
|CSDMS meeting city=Stennis Space Center<br />
|CSDMS meeting country=United States<br />
|CSDMS meeting state=Mississippi<br />
|CSDMS meeting email address=jbobelcz@gmail.com<br />
|CSDMS meeting phone=4845479143<br />
}}<br />
{{CSDMS meeting scholar and pre-meeting<br />
|CSDMS meeting pre-conference=None<br />
|CSDMS meeting post-conference=No<br />
}}<br />
{{CSDMS meeting select clinics1 2018<br />
|CSDMS_meeting_select_clinics1_2018=4) Sediment Experimentalist Network<br />
}}<br />
{{CSDMS meeting select clinics2 2018<br />
|CSDMS_meeting_select_clinics2_2018=3) Data for natural hazards<br />
}}<br />
{{CSDMS meeting select clinics3 2018<br />
|CSDMS_meeting_select_clinics3_2018=4) EarthLab<br />
}}<br />
{{CSDMS scholarships yes no<br />
|CSDMS meeting scholarships=No<br />
}}<br />
{{CSDMS meeting abstract yes no 2018<br />
|CSDMS meeting abstract submit=Yes<br />
}}<br />
{{CSDMS meeting abstract title temp2018<br />
|CSDMS meeting abstract title=Towards a Quantitative Understanding of Parameters Driving Submarine Slope Failure: A Data Mining and Machine Learning Approach<br />
}}<br />
{{CSDMS meeting authors template<br />
|CSDMS meeting coauthor first name abstract=Warren<br />
|CSDMS meeting coauthor last name abstract=T Wood<br />
|CSDMS meeting coauthor institute / Organization=Naval Research Lab<br />
|CSDMS meeting coauthor town-city=Stennis Space Center<br />
|CSDMS meeting coauthor country=United States<br />
|State=Mississippi<br />
|CSDMS meeting coauthor email address=warren.wood@nrlssc.navy.mil<br />
}}<br />
{{CSDMS meeting abstract template 2018<br />
|CSDMS meeting abstract=Submarine slope failure is a ubiquitous process and dominant pathway for sediment and organic carbon flux from continental margins to the deep sea. Slope failure occurs over a wide range of temporal and spatial scales, from small (10e4-10e5 m3/event), sub-annual failures on heavily sedimented river deltas to margin-altering and tsunamigenic (10-100 km3/event) open slope failures occurring on glacial-interglacial timescales. Despite their importance to basic (closing the global source-to-sink sediment budget) and applied (submarine geohazards) research, submarine slope failure frequency and magnitude on most continental margins remains poorly constrained. This is primarily due to difficulty in 1) directly observing events, and 2) reconstructing age and size, particularly in the geologic record. The state of knowledge regarding submarine slope failure preconditioning and triggering factors is more qualitative than quantitative; a vague hierarchy of factor importance has been established in most settings but slope failures cannot yet be forecasted or hindcasted from a priori knowledge of these factors.<br />
<br />
A new approach to address the knowledge gaps outlined above is using machine learning to quantitatively identify triggering and preconditioning factors that are most strongly correlated with submarine slope failure occurrence. This occurs in three general steps: 1) compile potential predictors of slope failure occurrence gridded and interpolated at desired resolution, 2) compile predictands (specific values that we wish to predict), and 3) recursively test predictor/predictand correlation with observed data until the strongest correlations are found. Potential predictors can be parsed into categories such as morphology (gradient, curvature, roughness), geology (clay fraction, grain size, sedimentation rate, fault proximity), and triggers (seismicity, significant wave height, river discharge). Predictands (i.e. training data) are various proxies for slope failure occurrence, including depth change between bathymetric surveys and sediment shear strength. The initial test sites are heavily sedimented, societally important river deltas, as they host both frequent slope failures and ample predictor/predictand measurements. Once predictors that strongly correlate with submarine slope failure occurrence are identified, this approach can be applied in more data-poor settings to further our current understanding of global submarine slope failure distribution, frequency, and magnitude.<br />
}}<br />
{{blank line template}}</div>Jobelczhttps://csdms.colorado.edu/csdms_wiki/index.php?title=User:Jobelcz&diff=86466User:Jobelcz2015-07-02T20:36:01Z<p>Jobelcz: Created page with "{{Signup information member |First name member=Jeffrey |Last name member=Obelcz |Institute member=Louisiana State University |Department member=Oceanography and Coastal Scienc..."</p>
<hr />
<div>{{Signup information member<br />
|First name member=Jeffrey<br />
|Last name member=Obelcz<br />
|Institute member=Louisiana State University<br />
|Department member=Oceanography and Coastal Sciences<br />
|Postal address 1 member=2185 Glasgow Avenue<br />
|City member=Baton Rouge<br />
|Postal code member=70808<br />
|Country member=United States<br />
|State member=Louisiana<br />
|Confirm email member=jbobelcz@coastal.edu<br />
|Working group member=Coastal Working Group<br />
|Emaillist group member=yes<br />
}}</div>Jobelcz