FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4366284807> ?p ?o ?g. }

• W4366284807 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We train and evaluate 136 statistical models with a rainfall dataset with five landslide-triggering rainfall events recorded near Sitka, Alaska, USA, as well as >6,000 days of non-triggering rainfall (2002–2020). We use Akaike, Bayesian, and leave-one-out information criteria to compare models trained on cumulative precipitation at timescales ranging from 1 hour to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as with testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without. Although frequentist and Bayesian inference produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. Three-hour precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale combined with the limited role of antecedent conditions reflects the rapid draining of porous colluvial soils on very steep hillslopes around Sitka. We use the resulting estimates of daily landslide probability to establish two decision boundaries for three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning “dashboard” system (sitkalandslide.org). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers." @default.
• W4366284807 created "2023-04-20" @default.
• W4366284807 date "2023-04-18" @default.
• W4366284807 modified "2023-09-29" @default.
• W4366284807 title "Comment on egusphere-2023-25" @default.
• W4366284807 doi "https://doi.org/10.5194/egusphere-2023-25-rc2" @default.
• W4366284807 hasPublicationYear "2023" @default.
• W4366284807 type Work @default.
• W4366284807 citedByCount "0" @default.
• W4366284807 crossrefType "peer-review" @default.
• W4366284807 hasBestOaLocation W43662848071 @default.
• W4366284807 hasConcept C105795698 @default.
• W4366284807 hasConcept C107673813 @default.
• W4366284807 hasConcept C126674687 @default.
• W4366284807 hasConcept C127313418 @default.
• W4366284807 hasConcept C151956035 @default.
• W4366284807 hasConcept C154945302 @default.
• W4366284807 hasConcept C160234255 @default.
• W4366284807 hasConcept C162376815 @default.
• W4366284807 hasConcept C186295008 @default.
• W4366284807 hasConcept C187320778 @default.
• W4366284807 hasConcept C197640229 @default.
• W4366284807 hasConcept C205649164 @default.
• W4366284807 hasConcept C2776214188 @default.
• W4366284807 hasConcept C33923547 @default.
• W4366284807 hasConcept C39432304 @default.
• W4366284807 hasConcept C41008148 @default.
• W4366284807 hasConceptScore W4366284807C105795698 @default.
• W4366284807 hasConceptScore W4366284807C107673813 @default.
• W4366284807 hasConceptScore W4366284807C126674687 @default.
• W4366284807 hasConceptScore W4366284807C127313418 @default.
• W4366284807 hasConceptScore W4366284807C151956035 @default.
• W4366284807 hasConceptScore W4366284807C154945302 @default.
• W4366284807 hasConceptScore W4366284807C160234255 @default.
• W4366284807 hasConceptScore W4366284807C162376815 @default.
• W4366284807 hasConceptScore W4366284807C186295008 @default.
• W4366284807 hasConceptScore W4366284807C187320778 @default.
• W4366284807 hasConceptScore W4366284807C197640229 @default.
• W4366284807 hasConceptScore W4366284807C205649164 @default.
• W4366284807 hasConceptScore W4366284807C2776214188 @default.
• W4366284807 hasConceptScore W4366284807C33923547 @default.
• W4366284807 hasConceptScore W4366284807C39432304 @default.
• W4366284807 hasConceptScore W4366284807C41008148 @default.
• W4366284807 hasLocation W43662848071 @default.
• W4366284807 hasOpenAccess W4366284807 @default.
• W4366284807 hasPrimaryLocation W43662848071 @default.
• W4366284807 hasRelatedWork W2078526825 @default.
• W4366284807 hasRelatedWork W2290754432 @default.
• W4366284807 hasRelatedWork W3124023584 @default.
• W4366284807 hasRelatedWork W3131352612 @default.
• W4366284807 hasRelatedWork W3145488977 @default.
• W4366284807 hasRelatedWork W3154076928 @default.
• W4366284807 hasRelatedWork W3197329739 @default.
• W4366284807 hasRelatedWork W4231471330 @default.
• W4366284807 hasRelatedWork W4236081058 @default.
• W4366284807 hasRelatedWork W4285588330 @default.
• W4366284807 isParatext "false" @default.
• W4366284807 isRetracted "false" @default.
• W4366284807 workType "peer-review" @default.