Vermont EPSCoR Publications and Products
] . Suspended Sediment Prediction. In: 2014 NEAEB. 2014 NEAEB. Burlington VT; 2014.
. Identification of patterns of hysteresis in suspended sediment-discharge relationships to infer watershed sediment dynamics. In: Lake Champlain Research Conference. Lake Champlain Research Conference. Burlington, VT: Lake Champlain Basin Program; 2018. Available from: http://www.lcbp.org/water-environment/data-monitoring/lake-champlain-research-conference/
. Using unmanned aircraft system (UAS) photogrammetry to monitor bank erosion along river corridors. In: Lake Champlain Research Conference. Lake Champlain Research Conference. Burlington, VT: Lake Champlain Basin Program; 2018. Available from: http://www.lcbp.org/water-environment/data-monitoring/lake-champlain-research-conference/
. Hydrological Event Detection & Analysis (HEDA) tool for streamflow and water quality time series. CUAHSI Conference on Hydroinformatics [Internet]. 2019 . Available from: https://www.cuahsi.org/uploads/pages/img/2019_Conference_on_Hydroinformatics_Program.pdf
. Unmanned Aircraft System (UAS) Photogrammetry for Tracking Streambank Erosion and Geomorphic Change along a Protected River Corridor. In: Eighth International Conference on Case Histories in Geotechnical Engineering. Eighth International Conference on Case Histories in Geotechnical Engineering. Philadelphia, PA: Geo-Institute of ASCE (American Society of Civil Engineers); 2019. Available from: https://ascelibrary.org/doi/10.1061/9780784482070.015
. Unraveling Sediment Dynamics Within Watersheds From Patterns in Suspended Sediment-Discharge Rrelationships. In: 2018 GSA (Geological Society of America) Northeastern Section 53rd Annual Meeting. 2018 GSA (Geological Society of America) Northeastern Section 53rd Annual Meeting. Burlington, VT: Geological Society of America (GSA); 2018. Available from: https://gsa.confex.com/gsa/2018NE/meetingapp.cgi/Paper/310311
. Monitoring Fluvial Geomorphic Change Using Unmanned Aircraft System (UAS) Photogrammetry and Laser Scanning. In: 2018 GSA (Geological Society of America) Northeastern Section 53rd Annual Meeting. 2018 GSA (Geological Society of America) Northeastern Section 53rd Annual Meeting. Burlington, VT: Geological Society of America (GSA); 2018. Available from: https://gsa.confex.com/gsa/2018NE/meetingapp.cgi/Paper/309724
. Prediction of suspended sediment in rivers using artificial neural networks: Implications for development of sediment budgets. In: 2013 AGU (American Geophysical Union) Fall Meeting. 2013 AGU (American Geophysical Union) Fall Meeting. San Francisco, CA: American Geophysical Union (AGU); 2013.
. Unraveling sediment dynamics in the Mad River watershed through event concentration-discharge relationships and multi-temporal UAS surveys. In: 2018 CERM (Catskill Environmental Research & Monitoring) Conference. 2018 CERM (Catskill Environmental Research & Monitoring) Conference. Highmount, NY: Ashokan Watershed Stream Management Program; 2018. Available from: http://ashokanstreams.org/wp-content/uploads/2016/09/5-Hamshaw_CERM2018.pdf
. Suspended Sediment Prediction Using Artificial Neural Networks and Local Hydrometeorological Data (M.S. Thesis). Burlington VT: University of Vermont; 2014.
. High Frequency Turbidity Monitoring to Quantify Sediment Loading in the Mad River. 2014 NEAEB Conference. 2014 [cited 0BC].
. Applying Deep Learning to Event Concentration-Discharge Hysteresis Patterns to Reveal Differences in Sediment Dynamics across Contrasting Watersheds. 2018 AGU (American Geophysical Union) Fall Meeting [Internet]. 2018 . Available from: https://agu.confex.com/agu/fm18/meetingapp.cgi/Paper/355901
. Watershed data science at the event scale: Revealing insights in watershed function through analysis of concentration-discharge relationships. In: 2020 AGU (American Geophysical Union) Fall Meeting. 2020 AGU (American Geophysical Union) Fall Meeting. Virtual: American Geophysical Union (AGU); 2020. Available from: https://agu.confex.com/agu/fm20/webprogram/Paper766889.html
. Watershed data science at the event scale: Machine learning for event concentration-discharge analysis. Virtual Summit: Incorporating Data Science and Open Science Techniques in Aquatic Research [Internet]. 2020 . Available from: https://freshwaterecology.wordpress.com/2020/07/08/conference-workshop-virtual-summit-incorporating-data-science-and-open-science-techniques-in-aquatic-research/
. Automating the Classification of Hysteresis in Event Concentration-Discharge Relationships. In: SEDHYD 2019. SEDHYD 2019. Reno, NV: SEDHYD, INC.; 2019. Available from: https://www.sedhyd.org/2019/openconf/modules/request.php?module=oc_program&action=view.php&id=70&file=1/70.pdf
. Comparison of Unmanned Aircraft Systems (UAS) to LIDAR for Streambank Erosion Measurement at the Site-Specific Scale. Vermont Geospatial Forum 2017 [Internet]. 2017 . Available from: http://vcgi.vermont.gov/event/forum_2017/poster_gallery
. Quantifying streambank erosion using unmanned aerial systems at the site-specific and river network scales. In: Geo-Congress 2017 (Geotechnical Frontiers). Geo-Congress 2017 (Geotechnical Frontiers). Orlando, FL; 2017.
. Application of unmanned aircraft system (UAS) for monitoring bank erosion along river corridors. Geomatics, Natural Hazards and Risk [Internet]. 2019 ;10(1):1285 - 1305. Available from: https://www.tandfonline.com/doi/full/10.1080/19475705.2019.1571533
. Suspended Sediment Prediction Using Artificial Neural Networks and Local Hydrometeorological Data. 2014 NEAEB Conference. 2014 .
. Sediment Loading and Sources in the Mad River: Implications for sediment-bound nutrient management. IAGLR 2015 [Internet]. 2015 . Available from: http://www.iaglr.org/conference/downloads/2015_program.pdf
. Using Distributed Continuous Turbidity Monitoring to Inform Sediment and Sediment-bound Nutrient Budgets in a Small Watershed. 2014 AGU (American Geophysical Union) Fall Meeting. 2014 .
. Geospatial and Temporal Analysis of Thyroid Cancer Incidence in a Rural Population. Thyroid [Internet]. 2015 ;25(7):812 - 822. Available from: http://online.liebertpub.com/doi/10.1089/thy.2015.0039
. A Tandem Evolutionary Algorithm for Identifying Causal Rules from Complex Data. Evolutionary Computation [Internet]. 2019 :1 - 32. Available from: https://www.mitpressjournals.org/doi/abs/10.1162/evco_a_00252
. A Novel Evolutionary Algorithm for Mining Noisy Survey Datasets with an Application Toward Combating Chagas Disease. Journal on Policy and Complex Systems. 2017 ;3(2).