Publications – Listening To Data

Articles

J. A. Duarte, A. D. González, and J. J. Gourley, “Wildfire burn scar encapsulation,” Optimization letters, 2021.
[Bibtex]

@article{duarteWildfireBurnScar2021,
title = {Wildfire Burn Scar Encapsulation},
author = {Duarte, Jorge A. and Gonz{\'a}lez, Andr{\'e}s D. and Gourley, Jonathan J.},
year = {2021},
month = oct,
journal = {Optimization Letters},
issn = {1862-4480},
doi = {10.1007/s11590-021-01800-6},
abstract = {Wildfires burn annually across the United States (US), which threaten those in close proximity to them. Due to drastic alterations of soil properties and to the land surfaces by these fires, risks of flash floods, debris flows, and severe erosion increases for these areas, which can have catastrophic consequences for biota, people and property. Computational tools, such as the WildfireRain algorithm, have been designed and implemented to assess the potential occurrence of debris flows over burn scars. However, in order to efficiently operate these tools, they require independent, non-overlapping buffers around burned areas to be defined, which is not a trivial task. In this paper we consider the problem of efficiently subsetting the conterminous US (CONUS) domain into optimal subdomains around burn scars, aiming to enable domain-wide WildfireRain product outputs to be used for operations by the National Weather Service (NWS). To achieve this, we define the Object Encapsulation Problem, where burn scars are represented by single-cell objects in a gridded domain, and circular buffers must be constructed around them. We propose a Linear Programming (LP) model that solves this problem efficiently. Optimal results produced using this model are presented for both a simplified synthetic data set, as well as for a subset of burn scars produced by severe wildfires in 2012 over the CONUS.},
copyright = {All rights reserved},
langid = {english},
file = {/home/jad/Zotero/storage/75UB3DV9/Duarte et al. - 2021 - Wildfire burn scar encapsulation.pdf}
}

M. Rahman Khan, J. J. Gourley, J. A. Duarte, H. Vergara, D. Wasielewski, P. Ayral, and J. W. Fulton, “Uncertainty in remote sensing of streams using noncontact radars,” Journal of hydrology, vol. 603, p. 126809, 2021.
[Bibtex]

@article{rahmankhanUncertaintyRemoteSensing2021,
title = {Uncertainty in Remote Sensing of Streams Using Noncontact Radars},
author = {Rahman Khan, Mushfiqur and Gourley, Jonathan J. and Duarte, Jorge A. and Vergara, Humberto and Wasielewski, Daniel and Ayral, Pierre-Alain and Fulton, John W.},
year = {2021},
month = aug,
journal = {Journal of Hydrology},
volume = {603},
pages = {126809},
issn = {0022-1694},
doi = {10.1016/j.jhydrol.2021.126809},
abstract = {Accounting for freshwater resources and monitoring floods are vital functions for societies throughout the world. Remote-sensing methods offer great prospects to expand stream monitoring in developing countries and to smaller, headwater streams that are largely ungauged worldwide. This study evaluates the potential to estimate discharge using eight radar units that have been installed over streams in diverse hydrologic and hydraulic settings across the United States. The research highlights error characteristics associated with the measurements of stage using pulsed wave radars, mean channel velocity from continuous wave Doppler radars, and their combined use to estimate discharge at sites that were collocated with conventional streamgauges. Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. A dry concrete, flume showed the temperature-dependent stage variations were no more than 2~cm. Surface velocity retrievals needed to be adjusted to represent the mean channel velocity when estimating discharge. Different approaches were evaluated and application of two different, depth-dependent adjustment factors was found to yield the most accurate estimates. This study found that it is possible to get accurate discharge estimates from noncontact radar measurements, providing cost-effective solutions for remote sensing of ungauged streams. Lastly, radar measurements of the raw variables (i.e., stage and surface velocity) can be used in an early alerting context to detect flash floods in ungauged streams.},
copyright = {All rights reserved},
langid = {english},
keywords = {Flash floods,Noncontact streamgauging,Remote sensing,Uncertainty estimation},
file = {/home/jad/Zotero/storage/PFTHKN6C/Rahman Khan et al. - 2021 - Uncertainty in remote sensing of streams using non.pdf;/home/jad/Zotero/storage/5BZNFL5A/S0022169421008593.html}
}

J. Duarte, P. E. Kirstetter, M. Saharia, J. J. Gourley, H. Vergara, and C. D. Nicholson, “Predicting flood responses from spatial rainfall variability and basin morphology through machine learning,” , p. 22179, 2020.
[Bibtex]

@article{duartePredictingFloodResponses2020,
title = {Predicting Flood Responses from Spatial Rainfall Variability and Basin Morphology through Machine Learning},
author = {Duarte, Jorge and Kirstetter, Pierre E. and Saharia, Manabendra and Gourley, Jonathan J. and Vergara, Humberto and Nicholson, Charles D.},
year = {2020},
month = may,
pages = {22179},
abstract = {Predicting flash floods at short time scales as well as their impacts is of vital interest to forecasters, emergency managers and community members alike. Particularly, characteristics such as location, timing, and duration are crucial for decision-making processes for the protection of lives, property and infrastructure. Even though these characteristics are primarily driven by the causative rainfall and basin geomorphology, untangling the complex interactions between precipitation and hydrological processes becomes challenging due to the lack of observational datasets which capture diverse conditions.This work follows upon previous efforts on incorporating spatial rainfall moments as viable predictors for flash flood event characteristics such as lag time and the exceedance of flood stage thresholds at gauged locations over the Conterminous United States (CONUS). These variables were modeled by applying various supervised machine learning techniques over a database of flood events. The data included morphological, climatological, streamflow and precipitation data from over 21,000 flood-producing rainfall events - that occurred over 900+ different basins throughout the CONUS between 2002-2011. This dataset included basin parameters and indices derived from radar-based precipitation, which represented sub-basin scale rainfall spatial variability for each storm event. Both classification and regression models were constructed, and variable importance analysis was performed in order to determine the relevant factors reflecting hydrometeorological processes. In this iteration, a closer look at model performance consistency and variable selection aims to further explore rainfall moments' explanatory power of flood characteristics.},
copyright = {All rights reserved},
annotation = {ADS Bibcode: 2020EGUGA..2222179D}
}

J. A. Duarte García, “Probabilistic characterization of floods from catchment-scale precipitation moments,” , 2019.
[Bibtex]

@article{duarte2019probabilistic,
title = {Probabilistic Characterization of Floods from Catchment-Scale Precipitation Moments},
author = {Duarte Garc{\'i}a, Jorge Alberto},
year = {2019},
copyright = {All rights reserved}
}

S. Merchán Rubiano, A. Beltrán Gómez, and J. Duarte García, “Engineering students\’ academic performance prediction using ICFES test scores and demo-graphic data,” Ingeniería solidaria, vol. 13, iss. 21, p. 53–61, 2017.
[Bibtex]

@article{rubiano2017engineering,
title = {Engineering Students{\' } Academic Performance Prediction Using {{ICFES}} Test Scores and Demo-Graphic Data},
author = {Merch{\'a}n Rubiano, Sandra and Beltr{\'a}n G{\'o}mez, Ad{\'a}n and Duarte Garc{\'i}a, Jorge},
year = {2017},
volume = {13},
pages = {53--61},
copyright = {All rights reserved},
journal = {Ingenier{\'i}a solidaria},
number = {21}
}

J. A. Duarte García, “Software defined radio for wi-fi jamming,” Https://www.researchgate.net/publication/301850218_software_defined_radio_for_wi-fi_jamming, iss. DOI: 10.13140/RG.2.2.23772.90240, 2016.
[Bibtex]

@article{duarte2016software,
title = {Software Defined Radio for Wi-Fi Jamming},
author = {Duarte Garc{\'i}a, Jorge A.},
year = {2016},
publisher = {{ResearchGate}},
copyright = {All rights reserved},
journal = {https://www.researchgate.net/publication/301850218\_Software\_Defined\_Radio\_for\_Wi-Fi\_Jamming},
number = {DOI: 10.13140/RG.2.2.23772.90240}
}

S. M. Merchan Rubiano and J. A. Duarte Garcia, “Analysis of data mining techniques for constructing a predictive model for academic performance,” Ieee latin america transactions, vol. 14, iss. 6, p. 2783–2788, 2016.
[Bibtex]

@article{rubiano2016analysis,
title = {Analysis of Data Mining Techniques for Constructing a Predictive Model for Academic Performance},
author = {Merchan Rubiano, Sandra Milena and Duarte Garcia, Jorge Alberto},
year = {2016},
volume = {14},
pages = {2783--2788},
publisher = {{IEEE}},
copyright = {All rights reserved},
journal = {IEEE Latin America Transactions},
number = {6}
}

In Proceedings

J. A. Duarte, P. Kirstetter, M. Saharia, J. J. Gourley, H. Vergara, and C. D. Nicholson, “Data-driven, physically-based characterization of floods accounting for sub-basin precipitation variability,” in 100th american meteorological society annual meeting, 2020.
[Bibtex]

@inproceedings{duarte2020data,
title = {Data-Driven, Physically-Based Characterization of Floods Accounting for Sub-Basin Precipitation Variability},
booktitle = {100th American Meteorological Society Annual Meeting},
author = {Duarte, Jorge A and Kirstetter, Pierre-Emmanuel and Saharia, Manabendra and Gourley, Jonathan J and Vergara, Humberto and Nicholson, Charles D},
year = {2020},
copyright = {All rights reserved},
organization = {{AMS}}
}

J. J. Gourley, K. W. Howard, P. Kirstetter, M. E. Weber, H. Vergara, J. A. Duarte, C. Marshall, and J. Hendricks, “Use of commercial, airborne weather radars to fill in operational network gaps,” in 100th american meteorological society annual meeting, 2020.
[Bibtex]

@inproceedings{gourley2020use,
title = {Use of Commercial, Airborne Weather Radars to Fill in Operational Network Gaps},
booktitle = {100th American Meteorological Society Annual Meeting},
author = {Gourley, Jonathan J and Howard, Kenneth W and Kirstetter, Pierre-Emmanuel and Weber, Mark E and Vergara, Humberto and Duarte, Jorge A and Marshall, Curtis and Hendricks, Jeannine},
year = {2020},
copyright = {All rights reserved},
organization = {{AMS}}
}

J. A. Duarte, J. J. Gourley, P. Gauthier, D. M. Staley, and H. Vergara, “Withdrawn: Development and implementation of tools to monitor and forecast post-wildfire debris flows,” in 99th american meteorological society annual meeting, 2019.
[Bibtex]

@inproceedings{duarte2019withdrawn,
title = {Withdrawn: {{Development}} and Implementation of Tools to Monitor and Forecast Post-Wildfire Debris Flows},
booktitle = {99th American Meteorological Society Annual Meeting},
author = {Duarte, Jorge A and Gourley, Jonathan J and Gauthier, Paul and Staley, Dennis M and Vergara, Humberto},
year = {2019},
copyright = {All rights reserved},
organization = {{AMS}}
}

J. A. Duarte García, “Ingeniería, ciencia de datos e investigación transdisciplinaria,” in Human-centered computing international summit, universidad el bosque, 2019.
[Bibtex]

@inproceedings{garcia2019ingenieria,
title = {Ingenier{\'i}a, Ciencia de Datos e Investigaci{\'o}n Transdisciplinaria},
booktitle = {Human-Centered Computing International Summit, Universidad El Bosque},
author = {Duarte Garc{\'i}a, Jorge A.},
year = {2019},
copyright = {All rights reserved}
}

D. J. Wasielewski, J. J. Gourley, J. A. Duarte, and N. Allaix, “Withdrawn: Stream bathymetry retrievals with the PinPoint bathymetry lidar,” in 99th american meteorological society annual meeting, 2019.
[Bibtex]

@inproceedings{wasielewski2019withdrawn,
title = {Withdrawn: {{Stream}} Bathymetry Retrievals with the {{PinPoint}} Bathymetry Lidar},
booktitle = {99th American Meteorological Society Annual Meeting},
author = {Wasielewski, Daniel J and Gourley, Jonathan J and Duarte, Jorge A and Allaix, Nad{\`e}ge},
year = {2019},
copyright = {All rights reserved},
organization = {{AMS}}
}

J. J. Gourley, H. Vergara, S. Martinaitis, Z. Flamig, and J. Duarte, “Identification of extreme rainfall amounts and concomitant flooding impacts at continental scale,” in AGU fall meeting abstracts, 2018.
[Bibtex]

@inproceedings{gourley2018identification,
title = {Identification of Extreme Rainfall Amounts and Concomitant Flooding Impacts at Continental Scale},
booktitle = {{{AGU}} Fall Meeting Abstracts},
author = {Gourley, Jonathan J and Vergara, HJ and Martinaitis, S and Flamig, Z and Duarte, J},
year = {2018},
copyright = {All rights reserved}
}

J. Gourley, D. Wasielewski, J. Fulton, J. Duarte, N. Allaix, S. Garnier, and P. Ayral, “Recent advances in estimating river properties using non-contact methods,” in EGU general assembly conference abstracts, 2018, p. 9980.
[Bibtex]

@inproceedings{gourley2018recent,
title = {Recent Advances in Estimating River Properties Using Non-Contact Methods},
booktitle = {{{EGU}} General Assembly Conference Abstracts},
author = {Gourley, Jonathan and Wasielewski, Daniel and Fulton, John and Duarte, Jorge and Allaix, Nad{\`e}ge and Garnier, Sacha and Ayral, Pierre-Alain},
year = {2018},
volume = {20},
pages = {9980},
copyright = {All rights reserved}
}

S. M. Merchan Rubiano and J. A. Duarte Garcia, “Formulation of a predictive model for academic performance based on students’ academic and demographic data,” in 2015 IEEE frontiers in education conference (FIE), 2015, p. 1–7.
[Bibtex]

@inproceedings{rubiano2015formulation,
title = {Formulation of a Predictive Model for Academic Performance Based on Students' Academic and Demographic Data},
booktitle = {2015 {{IEEE}} Frontiers in Education Conference ({{FIE}})},
author = {Merchan Rubiano, Sandra Milena and Duarte Garcia, Jorge Alberto},
year = {2015},
pages = {1--7},
copyright = {All rights reserved},
organization = {{IEEE}}
}