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Steven M. Quiring - One of the best experts on this subject based on the ideXlab platform.

  • Power Outage Estimation for Tropical Cyclones: Improved Accuracy with Simpler Models
    Risk Analysis, 2014
    Co-Authors: Roshanak Nateghi, Seth D Guikema, Steven M. Quiring
    Abstract:

    In this article, we discuss an outage-forecasting model that we have developed. This model uses very few input variables to estimate hurricane-induced outages prior to landfall with great predictive accuracy. We also show the results for a series of simpler models that use only publicly available data and can still estimate outages with reasonable accuracy. The intended users of these models are emergency response planners within power utilities and related government agencies. We developed our models based on the method of random forest, using data from a power distribution system serving two states in the Gulf Coast Region of the United States. We also show that estimates of system reliability based on wind speed alone are not sufficient for adequately capturing the reliability of system components. We demonstrate that a multivariate approach can produce more accurate power outage predictions.

  • estimating the spatial distribution of power outages during hurricanes in the Gulf Coast Region
    Reliability Engineering & System Safety, 2009
    Co-Authors: Seth D Guikema, David V Rosowsky, Steven M. Quiring, Rachel A Davidson
    Abstract:

    Hurricanes have caused severe damage to the electric power system throughout the Gulf Coast Region of the US, and electric power is critical to post-hurricane disaster response as well as to long-term recovery for impacted areas. Managing power outage risk and preparing for post-storm recovery efforts requires accurate methods for estimating the number and location of power outages. This paper builds on past work on statistical power outage estimation models to develop, test, and demonstrate a statistical power outage risk estimation model for the Gulf Coast Region of the US. Previous work used binary hurricane-indicator variables representing particular hurricanes in order to achieve a good fit to the past data. To use these models for predicting power outages during future hurricanes, one must implicitly assume that an approaching hurricane is similar to the average of the past hurricanes. The model developed in this paper replaces these indicator variables with physically measurable variables, enabling future predictions to be based on only well-understood characteristics of hurricanes. The models were developed using data about power outages during nine hurricanes in three states served by a large, investor-owned utility company in the Gulf Coast Region.

  • Improving the predictive accuracy of hurricane power outage forecasts using generalized additive models
    Risk Analysis, 2009
    Co-Authors: Seung Ryong Han, Seth D Guikema, Steven M. Quiring
    Abstract:

    Electric power is a critical infrastructure service after hurricanes, and rapid restoration of electric power is important in order to minimize losses in the impacted areas. However, rapid restoration of electric power after a hurricane depends on obtaining the necessary resources, primarily repair crews and materials, before the hurricane makes landfall and then appropriately deploying these resources as soon as possible after the hurricane. This, in turn, depends on having sound estimates of both the overall severity of the storm and the relative risk of power outages in different areas. Past studies have developed statistical, regression-based approaches for estimating the number of power outages in advance of an approaching hurricane. However, these approaches have either not been applicable for future events or have had lower predictive accuracy than desired. This article shows that a different type of regression model, a generalized additive model (GAM), can outperform the types of models used previously. This is done by developing and validating a GAM based on power outage data during past hurricanes in the Gulf Coast Region and comparing the results from this model to the previously used generalized linear models.

Gloria I Lopez - One of the best experts on this subject based on the ideXlab platform.

  • OSL-based lateral progradation and aeolian sediment accumulation rates for the Apalachicola Barrier Island Complex, North Gulf of Mexico, Florida
    2020
    Co-Authors: W J Rink, Gloria I Lopez
    Abstract:

    Vertical sediment cores in five separate beach ridge complexes along the north-east Gulf of Mexico Coast were recovered and dated using optically stimulated luminescence (OSL) dating of quartz: these are located on Cape San Blas (CSB), Little St. George Island (LSGI), Richardson's Hammock (RH), St. Joseph Peninsula (SJP) and Saint Vincent Island (SVI). All of these landforms are Coastal barrier systems situated along a 100 km stretch of the Florida Panhandle, U.S.A. Two samples were collected for dating from each core. Ridge accumulation rates (RAR) associated with lateral progradation were calculated from the dated samples. We also determined average sediment accumulation rates (ASAR) for two intervals within each sediment core. All OSL ages within the sediment cores were found to be in stratigraphic order or in a few cases statistically indistinguishable. Moreover, all dated ridges were found to be in correct temporal sequence based on their geomorphic positions. Rapidly accreted sequences were found to be backed by St. Joseph Bay in the western Region of the study area. More slowly accreted sequences were associated with the more eastern stretches of the study area backed by St. Vincent Sound and Apalachicola Bay. Our ASAR results are in accord with an Australian study of modern dune accumulation. Perhaps our most important finding is that in the barrier island environments of this north-eastern Gulf Coast Region, aeolian sedimentation continues well after full vegetative cover develops and stranding of landward ridges takes place. This confirms our similar earlier observation on SV

  • osl based lateral progradation and aeolian sediment accumulation rates for the apalachicola barrier island complex north Gulf of mexico florida
    Geomorphology, 2010
    Co-Authors: W J Rink, Gloria I Lopez
    Abstract:

    article i nfo Vertical sediment cores in five separate beach ridge complexes along the north-east Gulf of Mexico Coast were recovered and dated using optically stimulated luminescence (OSL) dating of quartz: these are located on Cape San Blas (CSB), Little St. George Island (LSGI), Richardson's Hammock (RH), St. Joseph Peninsula (SJP) and Saint Vincent Island (SVI). All of these landforms are Coastal barrier systems situated along a 100 km stretch of the Florida Panhandle, U.S.A. Two samples were collected for dating from each core. Ridge accumulation rates (RAR) associated with lateral progradation were calculated from the dated samples. We also determined average sediment accumulation rates (ASAR) for two intervals within each sediment core. All OSL ages within the sediment cores were found to be in stratigraphic order or in a few cases statistically indistinguishable. Moreover, all dated ridges were found to be in correct temporal sequence based on their geomorphic positions. Rapidly accreted sequences were found to be backed by St. Joseph Bay in the western Region of the study area. More slowly accreted sequences were associated with the more eastern stretches of the study area backed by St. Vincent Sound and Apalachicola Bay. Our ASAR results are in accord with an Australian study of modern dune accumulation. Perhaps our most important finding is that in the barrier island environments of this north-eastern Gulf Coast Region, aeolian sedimentation continues well after full vegetative cover develops and stranding of landward ridges takes place. This confirms our similar earlier observation on SVI (Lopez and Rink, 2008). We find that up to approximately one order of magnitude lower sedimentation rates occur after an initial period of more rapid aeolian accumulation for the vertical intervals studied in foredune ridges. Lateral progradation rates of ridge sequences were highly variable within the study area, ranging from 92 to 848 m/100 years, but we did find agreement between some of our slower ridge accumulation rates and those in other areas of Florida and around the world determined using OSL and 14 C dating. We conclude that OSL may be used in a Regional context to establish variation in progradation rates and aeolian accumulation that varies among systems.

Seth D Guikema - One of the best experts on this subject based on the ideXlab platform.

  • Power Outage Estimation for Tropical Cyclones: Improved Accuracy with Simpler Models
    Risk Analysis, 2014
    Co-Authors: Roshanak Nateghi, Seth D Guikema, Steven M. Quiring
    Abstract:

    In this article, we discuss an outage-forecasting model that we have developed. This model uses very few input variables to estimate hurricane-induced outages prior to landfall with great predictive accuracy. We also show the results for a series of simpler models that use only publicly available data and can still estimate outages with reasonable accuracy. The intended users of these models are emergency response planners within power utilities and related government agencies. We developed our models based on the method of random forest, using data from a power distribution system serving two states in the Gulf Coast Region of the United States. We also show that estimates of system reliability based on wind speed alone are not sufficient for adequately capturing the reliability of system components. We demonstrate that a multivariate approach can produce more accurate power outage predictions.

  • estimating the spatial distribution of power outages during hurricanes in the Gulf Coast Region
    Reliability Engineering & System Safety, 2009
    Co-Authors: Seth D Guikema, David V Rosowsky, Steven M. Quiring, Rachel A Davidson
    Abstract:

    Hurricanes have caused severe damage to the electric power system throughout the Gulf Coast Region of the US, and electric power is critical to post-hurricane disaster response as well as to long-term recovery for impacted areas. Managing power outage risk and preparing for post-storm recovery efforts requires accurate methods for estimating the number and location of power outages. This paper builds on past work on statistical power outage estimation models to develop, test, and demonstrate a statistical power outage risk estimation model for the Gulf Coast Region of the US. Previous work used binary hurricane-indicator variables representing particular hurricanes in order to achieve a good fit to the past data. To use these models for predicting power outages during future hurricanes, one must implicitly assume that an approaching hurricane is similar to the average of the past hurricanes. The model developed in this paper replaces these indicator variables with physically measurable variables, enabling future predictions to be based on only well-understood characteristics of hurricanes. The models were developed using data about power outages during nine hurricanes in three states served by a large, investor-owned utility company in the Gulf Coast Region.

  • Improving the predictive accuracy of hurricane power outage forecasts using generalized additive models
    Risk Analysis, 2009
    Co-Authors: Seung Ryong Han, Seth D Guikema, Steven M. Quiring
    Abstract:

    Electric power is a critical infrastructure service after hurricanes, and rapid restoration of electric power is important in order to minimize losses in the impacted areas. However, rapid restoration of electric power after a hurricane depends on obtaining the necessary resources, primarily repair crews and materials, before the hurricane makes landfall and then appropriately deploying these resources as soon as possible after the hurricane. This, in turn, depends on having sound estimates of both the overall severity of the storm and the relative risk of power outages in different areas. Past studies have developed statistical, regression-based approaches for estimating the number of power outages in advance of an approaching hurricane. However, these approaches have either not been applicable for future events or have had lower predictive accuracy than desired. This article shows that a different type of regression model, a generalized additive model (GAM), can outperform the types of models used previously. This is done by developing and validating a GAM based on power outage data during past hurricanes in the Gulf Coast Region and comparing the results from this model to the previously used generalized linear models.

W J Rink - One of the best experts on this subject based on the ideXlab platform.

  • OSL-based lateral progradation and aeolian sediment accumulation rates for the Apalachicola Barrier Island Complex, North Gulf of Mexico, Florida
    2020
    Co-Authors: W J Rink, Gloria I Lopez
    Abstract:

    Vertical sediment cores in five separate beach ridge complexes along the north-east Gulf of Mexico Coast were recovered and dated using optically stimulated luminescence (OSL) dating of quartz: these are located on Cape San Blas (CSB), Little St. George Island (LSGI), Richardson's Hammock (RH), St. Joseph Peninsula (SJP) and Saint Vincent Island (SVI). All of these landforms are Coastal barrier systems situated along a 100 km stretch of the Florida Panhandle, U.S.A. Two samples were collected for dating from each core. Ridge accumulation rates (RAR) associated with lateral progradation were calculated from the dated samples. We also determined average sediment accumulation rates (ASAR) for two intervals within each sediment core. All OSL ages within the sediment cores were found to be in stratigraphic order or in a few cases statistically indistinguishable. Moreover, all dated ridges were found to be in correct temporal sequence based on their geomorphic positions. Rapidly accreted sequences were found to be backed by St. Joseph Bay in the western Region of the study area. More slowly accreted sequences were associated with the more eastern stretches of the study area backed by St. Vincent Sound and Apalachicola Bay. Our ASAR results are in accord with an Australian study of modern dune accumulation. Perhaps our most important finding is that in the barrier island environments of this north-eastern Gulf Coast Region, aeolian sedimentation continues well after full vegetative cover develops and stranding of landward ridges takes place. This confirms our similar earlier observation on SV

  • osl based lateral progradation and aeolian sediment accumulation rates for the apalachicola barrier island complex north Gulf of mexico florida
    Geomorphology, 2010
    Co-Authors: W J Rink, Gloria I Lopez
    Abstract:

    article i nfo Vertical sediment cores in five separate beach ridge complexes along the north-east Gulf of Mexico Coast were recovered and dated using optically stimulated luminescence (OSL) dating of quartz: these are located on Cape San Blas (CSB), Little St. George Island (LSGI), Richardson's Hammock (RH), St. Joseph Peninsula (SJP) and Saint Vincent Island (SVI). All of these landforms are Coastal barrier systems situated along a 100 km stretch of the Florida Panhandle, U.S.A. Two samples were collected for dating from each core. Ridge accumulation rates (RAR) associated with lateral progradation were calculated from the dated samples. We also determined average sediment accumulation rates (ASAR) for two intervals within each sediment core. All OSL ages within the sediment cores were found to be in stratigraphic order or in a few cases statistically indistinguishable. Moreover, all dated ridges were found to be in correct temporal sequence based on their geomorphic positions. Rapidly accreted sequences were found to be backed by St. Joseph Bay in the western Region of the study area. More slowly accreted sequences were associated with the more eastern stretches of the study area backed by St. Vincent Sound and Apalachicola Bay. Our ASAR results are in accord with an Australian study of modern dune accumulation. Perhaps our most important finding is that in the barrier island environments of this north-eastern Gulf Coast Region, aeolian sedimentation continues well after full vegetative cover develops and stranding of landward ridges takes place. This confirms our similar earlier observation on SVI (Lopez and Rink, 2008). We find that up to approximately one order of magnitude lower sedimentation rates occur after an initial period of more rapid aeolian accumulation for the vertical intervals studied in foredune ridges. Lateral progradation rates of ridge sequences were highly variable within the study area, ranging from 92 to 848 m/100 years, but we did find agreement between some of our slower ridge accumulation rates and those in other areas of Florida and around the world determined using OSL and 14 C dating. We conclude that OSL may be used in a Regional context to establish variation in progradation rates and aeolian accumulation that varies among systems.

Rachel A Davidson - One of the best experts on this subject based on the ideXlab platform.

  • estimating the spatial distribution of power outages during hurricanes in the Gulf Coast Region
    Reliability Engineering & System Safety, 2009
    Co-Authors: Seth D Guikema, David V Rosowsky, Steven M. Quiring, Rachel A Davidson
    Abstract:

    Hurricanes have caused severe damage to the electric power system throughout the Gulf Coast Region of the US, and electric power is critical to post-hurricane disaster response as well as to long-term recovery for impacted areas. Managing power outage risk and preparing for post-storm recovery efforts requires accurate methods for estimating the number and location of power outages. This paper builds on past work on statistical power outage estimation models to develop, test, and demonstrate a statistical power outage risk estimation model for the Gulf Coast Region of the US. Previous work used binary hurricane-indicator variables representing particular hurricanes in order to achieve a good fit to the past data. To use these models for predicting power outages during future hurricanes, one must implicitly assume that an approaching hurricane is similar to the average of the past hurricanes. The model developed in this paper replaces these indicator variables with physically measurable variables, enabling future predictions to be based on only well-understood characteristics of hurricanes. The models were developed using data about power outages during nine hurricanes in three states served by a large, investor-owned utility company in the Gulf Coast Region.