FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3162738681> ?p ?o ?g. }

• W3162738681 abstract "Coastal counties in the United States account for less than 10% of the nation’s land mass. Yet, approximately 40% of the country’s population, or over 127 million people, live in these areas. The population density of coastal counties is 461 people per square mile, much larger than the nation’s average population density of 87 people per square mile. Coasts also present the logistic benefit of allowing the transportation of goods between countries and continents through maritime ports. However, the increase in coastal population and economic activity means an increased exposure and vulnerability to potential natural hazards, such as hurricanes and tropical storms. These weather events are powerful, with the capacity to devastate coastal regions. Therefore, understanding these potentially catastrophic events is critical to assess vulnerability and support informed decision-making at local, state, and federal levels. This research provides valuable insights related to the characteristics of tropical cyclones and to their potential impacts to the coastal United States.First, an extensive review of the literature related to maritime supply chain resilience and the impacts of port disruptions to the maritime supply chain is performed. Ports are complex enterprises, comprised of a wide variety of stakeholders and subject to risks of many kinds, both man-made and natural hazards. This review allowed the identification of gaps of knowledge to be explored on the topic of maritime supply chain resilience. One of the gaps is the lack of a clearly quantifiable metric for the impacts of one of the most common sources of weather disruptions: hurricane and tropical storms. Albeit the immediate impacts are limited to areas prone to these events, tropical cyclones have been known to impact extensive areas and cause long lasting negative effects.Second, machine learning is used to rigorously explore and quantify the relationship of tropical cyclone characteristics and their destructive outcomes on the coast of the United States. Historical data on hurricanes and tropical storms is identified and curated to support supervised learning. A novel Storm Damage Ratio is introduced to address the inherent challenge of comparing damage to regions with distinct assets and population. Multiple mathematical models to predict economic impacts from tropical events are created using machine learning methods and the results are compared. Additionally, the storm features that most influence the accuracy of predictions are identified and ranked.The third research component consists in analyzing coastal vulnerability to tropical cyclones at the state-level by providing mechanisms to account for uncertainty in studying the destructive potential of storms, supporting the decision-making process to improve community resilience. The previously developed concept of Storm Damage Ratio is extended, creating the Local Storm Damage Ratio, which assess the destructive potential of storms with respect to intrinsic characteristics, regardless of the local…" @default.
• W3162738681 created "2021-05-24" @default.
• W3162738681 creator A5021267163 @default.
• W3162738681 date "2020-12-01" @default.
• W3162738681 modified "2023-09-27" @default.
• W3162738681 title "Coastal vulnerability: Impact of port disruptions and the economic impacts of tropical cyclones" @default.
• W3162738681 hasPublicationYear "2020" @default.
• W3162738681 type Work @default.
• W3162738681 sameAs 3162738681 @default.
• W3162738681 citedByCount "0" @default.
• W3162738681 crossrefType "journal-article" @default.
• W3162738681 hasAuthorship W3162738681A5021267163 @default.
• W3162738681 hasConcept C107826830 @default.
• W3162738681 hasConcept C119599485 @default.
• W3162738681 hasConcept C121332964 @default.
• W3162738681 hasConcept C127413603 @default.
• W3162738681 hasConcept C144024400 @default.
• W3162738681 hasConcept C147176958 @default.
• W3162738681 hasConcept C149923435 @default.
• W3162738681 hasConcept C153294291 @default.
• W3162738681 hasConcept C188897 @default.
• W3162738681 hasConcept C205649164 @default.
• W3162738681 hasConcept C2779585090 @default.
• W3162738681 hasConcept C2908647359 @default.
• W3162738681 hasConcept C32802771 @default.
• W3162738681 hasConcept C38652104 @default.
• W3162738681 hasConcept C39410599 @default.
• W3162738681 hasConcept C39432304 @default.
• W3162738681 hasConcept C41008148 @default.
• W3162738681 hasConcept C91375879 @default.
• W3162738681 hasConcept C95713431 @default.
• W3162738681 hasConcept C97355855 @default.
• W3162738681 hasConceptScore W3162738681C107826830 @default.
• W3162738681 hasConceptScore W3162738681C119599485 @default.
• W3162738681 hasConceptScore W3162738681C121332964 @default.
• W3162738681 hasConceptScore W3162738681C127413603 @default.
• W3162738681 hasConceptScore W3162738681C144024400 @default.
• W3162738681 hasConceptScore W3162738681C147176958 @default.
• W3162738681 hasConceptScore W3162738681C149923435 @default.
• W3162738681 hasConceptScore W3162738681C153294291 @default.
• W3162738681 hasConceptScore W3162738681C188897 @default.
• W3162738681 hasConceptScore W3162738681C205649164 @default.
• W3162738681 hasConceptScore W3162738681C2779585090 @default.
• W3162738681 hasConceptScore W3162738681C2908647359 @default.
• W3162738681 hasConceptScore W3162738681C32802771 @default.
• W3162738681 hasConceptScore W3162738681C38652104 @default.
• W3162738681 hasConceptScore W3162738681C39410599 @default.
• W3162738681 hasConceptScore W3162738681C39432304 @default.
• W3162738681 hasConceptScore W3162738681C41008148 @default.
• W3162738681 hasConceptScore W3162738681C91375879 @default.
• W3162738681 hasConceptScore W3162738681C95713431 @default.
• W3162738681 hasConceptScore W3162738681C97355855 @default.
• W3162738681 hasLocation W31627386811 @default.
• W3162738681 hasOpenAccess W3162738681 @default.
• W3162738681 hasPrimaryLocation W31627386811 @default.
• W3162738681 hasRelatedWork W1968627586 @default.
• W3162738681 hasRelatedWork W2090813118 @default.
• W3162738681 hasRelatedWork W2097219321 @default.
• W3162738681 hasRelatedWork W2097949844 @default.
• W3162738681 hasRelatedWork W2143432534 @default.
• W3162738681 hasRelatedWork W2227228305 @default.
• W3162738681 hasRelatedWork W2254138843 @default.
• W3162738681 hasRelatedWork W2256582387 @default.
• W3162738681 hasRelatedWork W2287749509 @default.
• W3162738681 hasRelatedWork W2507653524 @default.
• W3162738681 hasRelatedWork W2508849058 @default.
• W3162738681 hasRelatedWork W2738381374 @default.
• W3162738681 hasRelatedWork W2792679032 @default.
• W3162738681 hasRelatedWork W2885576632 @default.
• W3162738681 hasRelatedWork W2955893349 @default.
• W3162738681 hasRelatedWork W2964876791 @default.
• W3162738681 hasRelatedWork W3039898766 @default.
• W3162738681 hasRelatedWork W3130665033 @default.
• W3162738681 hasRelatedWork W3159888619 @default.
• W3162738681 hasRelatedWork W3201671711 @default.
• W3162738681 isParatext "false" @default.
• W3162738681 isRetracted "false" @default.
• W3162738681 magId "3162738681" @default.
• W3162738681 workType "article" @default.