Item talk:Q49376
From geokb
orcid:
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Smith assertion-origin-orcid: host: orcid.org path: 0000-0003-3124-8255 uri: https://orcid.org/0000-0003-3124-8255 source-client-id: host: orcid.org path: 0000-0002-5982-8983 uri: https://orcid.org/client/0000-0002-5982-8983 source-name: value: Scopus - Elsevier source-orcid: null title: subtitle: null title: value: Low-Temperature geothermal energy characterization by play fairway analysis for the appalachian basin of New York, Pennsylvania and West Virginia translated-title: null type: conference-paper url: value: http://www.scopus.com/inward/record.url?eid=2-s2.0-84964001479&partnerID=MN8TOARS visibility: public - external-ids: external-id: - external-id-normalized: transient: true value: 2-s2.0-84937960802 external-id-normalized-error: null external-id-relationship: self external-id-type: eid external-id-url: null external-id-value: 2-s2.0-84937960802 last-modified-date: value: 1653516519251 work-summary: - created-date: value: 1502419178794 display-index: '0' external-ids: external-id: - external-id-normalized: transient: true value: 2-s2.0-84937960802 external-id-normalized-error: null external-id-relationship: self external-id-type: eid external-id-url: null external-id-value: 2-s2.0-84937960802 journal-title: value: Transactions - Geothermal Resources Council last-modified-date: value: 1653516519251 path: /0000-0003-3124-8255/work/35910691 publication-date: day: null month: null year: value: '2014' put-code: 35910691 source: assertion-origin-client-id: null assertion-origin-name: value: Jared D. Smith assertion-origin-orcid: host: orcid.org path: 0000-0003-3124-8255 uri: https://orcid.org/0000-0003-3124-8255 source-client-id: host: orcid.org path: 0000-0002-5982-8983 uri: https://orcid.org/client/0000-0002-5982-8983 source-name: value: Scopus - Elsevier source-orcid: null title: subtitle: null title: value: Geothermal energy characterization in the appalachian basin of New York and Pennsylvania translated-title: null type: conference-paper url: value: http://www.scopus.com/inward/record.url?eid=2-s2.0-84937960802&partnerID=MN8TOARS visibility: public last-modified-date: value: 1656427092822 path: /0000-0003-3124-8255/works history: claimed: true completion-date: null creation-method: DIRECT deactivation-date: null last-modified-date: value: 1693230992976 source: null submission-date: value: 1502418185916 verified-email: true verified-primary-email: true person: addresses: address: - country: value: US created-date: value: 1502419253805 display-index: 1 last-modified-date: value: 1502419688510 path: /0000-0003-3124-8255/address/760597 put-code: 760597 source: assertion-origin-client-id: null assertion-origin-name: null assertion-origin-orcid: null source-client-id: null source-name: value: Jared D. Smith source-orcid: host: orcid.org path: 0000-0003-3124-8255 uri: https://orcid.org/0000-0003-3124-8255 visibility: public last-modified-date: value: 1502419688510 path: /0000-0003-3124-8255/address biography: content: 'My research interests include mathematical modeling and spatial statistical analysis of environmental, earth-energy, and water resources systems.
Water and natural resources systems management: multiobjective spatial decision
analysis, risk analysis, and uncertainty analysis.
Geothermal energy: resource assessment, sedimentary basins, reservoir thermal-hydraulic
modeling, direct-use, bottom-hole temperature corrections, exploratory spatial
data analysis.
My research has addressed exploratory spatial data analysis for geothermal
datasets, stochastic methods for low-temperature geothermal resource assessment
models, and stochastic methods for probabilistic assessment of geothermal
reservoir models. These methods were applied in several research projects
that evaluated the feasibility of geothermal direct-use heat utilization
systems in the Appalachian Basin.'
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usgs_staff_profile:
meta: status_code: 200 timestamp: '2023-09-30T17:09:38.032849' url: https://www.usgs.gov/staff-profiles/jared-smith profile: abstracts: - "\u200BCluster Analysis and Prediction of Flood Flow Metrics for Minimally Altered\ \ Catchments in the Conterminous United States, HydroML Symposium 2022" - Discovering Flood Regions and Predicting Flood Flow Metrics to Inform Bridge Scour Studies in the Conterminous United States, National Hydraulic Engineering Conference, 2022 affiliations: [] education: - "\u200BPh.D. Environmental and Water Resources Systems Engineering, Cornell\ \ University, 2019" - M.S. Environmental and Water Resources Systems Engineering, Cornell University, 2016 - B.S. Environmental Engineering, Clarkson University, 2013 email: jsmith@usgs.gov expertise_terms: - Model Diagnostics - Machine Learning - Hydrologic Statistics - Optimization - Water Resource Management - Surface Water - Geothermal Resource Assessment - Heat Flow honors: [] intro_statements: - Jared D. Smith, Ph.D., is a Machine Learning Specialist in the USGS Water Resources Mission Area. He is based in Reston, VA. name: Jared Smith name_qualifier: null orcid: 0000-0003-3124-8255 organization_link: https://www.usgs.gov/mission-areas/water-resources organization_name: Water Resources Mission Area personal_statement: Jared has a background in environmental systems engineering, spatial data analysis, and statistics. His previous research has coupled physical and mathematical models with statistical analyses to inform planning and management decisions for environmental, earth-energy, and water resources systems. Jared joined the USGS in 2021 after completing a postdoc at The University of Virginia, where he developed green infrastructure portfolio optimizations that were designed to be robust to Bayesian-estimated parametric uncertainty of a watershed model. Jared completed his Ph.D. at Cornell University, where his research addressed Appalachian Basin geothermal resource assessment and subsequent uncertainty assessments for deep geothermal district heating projects at the Cornell and West Virginia University campuses. Previous work has also addressed ice jam flood frequency analysis under climate change, applied to the Peace-Athabasca Delta in Canada. professional_experience: [] title: Machine Learning Specialist
