FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3111782250> ?p ?o ?g. }

• W3111782250 endingPage "105979" @default.
• W3111782250 startingPage "105979" @default.
• W3111782250 abstract "In South Korea, the risk of debris-flow is relatively high due to the country's vast mountainous topographical features and intense continuous rainfall during the summer. Debris-flows can result in the loss of human life and severe property damage, which can be made worse due to the poor spatiotemporal predictability of such hazards. Therefore, it is essential to research the preemptive prediction and mitigation of debris-flow hazards. For this purpose, this study developed an ANN model to predict the debris-flow volume based on 63 historical events. By considering the morphology, rainfall, and geology characteristics of the studied area in central South Korea, the data of 15 debris-flow predisposing factors were obtained. Among these data, four predisposing factors (watershed area, channel length, watershed relief, and rainfall data) were selected based on Pearson's correlation analysis to check for significant correlations with the debris-flow volume. To determine the best performing ANN model, a validation testing was carried out involving ten-fold cross-validation with MSE and R2 using both training and validation datasets, which were randomly split into a 7:3 ratio. The model performance validation results showed that an ANN model with two hidden neurons (4×2×1 architecture) had the highest R2 value (0.828) and the lowest MSE (0.022). In addition, in a comparative study with other existing regression models, the ANN model showed better results in terms of adjusted R2 value (0.911) using all datasets. Furthermore, 94% of the observed debris-flow volumes from the ANN model were within 1:2 and 2:1 lines of the predicted volumes. The results of this study have shown the potentiality of the developed ANN model to be a useful resource for decision-making and designing barriers in areas prone to debris-flows in South Korea." @default.
• W3111782250 created "2020-12-21" @default.
• W3111782250 creator A5005128155 @default.
• W3111782250 creator A5015166385 @default.
• W3111782250 creator A5028585938 @default.
• W3111782250 creator A5037664989 @default.
• W3111782250 creator A5041768348 @default.
• W3111782250 creator A5084549334 @default.
• W3111782250 date "2021-02-01" @default.
• W3111782250 modified "2023-10-14" @default.
• W3111782250 title "An artificial neural network model to predict debris-flow volumes caused by extreme rainfall in the central region of South Korea" @default.
• W3111782250 cites W1606571603 @default.
• W3111782250 cites W1965843432 @default.
• W3111782250 cites W1976972127 @default.
• W3111782250 cites W1992860865 @default.
• W3111782250 cites W1993284672 @default.
• W3111782250 cites W1998743274 @default.
• W3111782250 cites W2011373971 @default.
• W3111782250 cites W2012118327 @default.
• W3111782250 cites W2028124403 @default.
• W3111782250 cites W2043356828 @default.
• W3111782250 cites W2044622173 @default.
• W3111782250 cites W2046361090 @default.
• W3111782250 cites W2049601606 @default.
• W3111782250 cites W2056063882 @default.
• W3111782250 cites W2060702643 @default.
• W3111782250 cites W2079019836 @default.
• W3111782250 cites W2090105324 @default.
• W3111782250 cites W2103496339 @default.
• W3111782250 cites W2128332356 @default.
• W3111782250 cites W2154816843 @default.
• W3111782250 cites W2155889930 @default.
• W3111782250 cites W2166976873 @default.
• W3111782250 cites W2531956991 @default.
• W3111782250 cites W2756037381 @default.
• W3111782250 cites W2898366807 @default.
• W3111782250 cites W2900013348 @default.
• W3111782250 cites W2911546748 @default.
• W3111782250 cites W2950260163 @default.
• W3111782250 cites W2950811860 @default.
• W3111782250 cites W2997942134 @default.
• W3111782250 cites W3015671262 @default.
• W3111782250 doi "https://doi.org/10.1016/j.enggeo.2020.105979" @default.
• W3111782250 hasPublicationYear "2021" @default.
• W3111782250 type Work @default.
• W3111782250 sameAs 3111782250 @default.
• W3111782250 citedByCount "13" @default.
• W3111782250 countsByYear W31117822502021 @default.
• W3111782250 countsByYear W31117822502022 @default.
• W3111782250 countsByYear W31117822502023 @default.
• W3111782250 crossrefType "journal-article" @default.
• W3111782250 hasAuthorship W3111782250A5005128155 @default.
• W3111782250 hasAuthorship W3111782250A5015166385 @default.
• W3111782250 hasAuthorship W3111782250A5028585938 @default.
• W3111782250 hasAuthorship W3111782250A5037664989 @default.
• W3111782250 hasAuthorship W3111782250A5041768348 @default.
• W3111782250 hasAuthorship W3111782250A5084549334 @default.
• W3111782250 hasConcept C105795698 @default.
• W3111782250 hasConcept C119857082 @default.
• W3111782250 hasConcept C122058494 @default.
• W3111782250 hasConcept C127313418 @default.
• W3111782250 hasConcept C150547873 @default.
• W3111782250 hasConcept C153294291 @default.
• W3111782250 hasConcept C187320778 @default.
• W3111782250 hasConcept C197640229 @default.
• W3111782250 hasConcept C205649164 @default.
• W3111782250 hasConcept C2776023875 @default.
• W3111782250 hasConcept C2776643431 @default.
• W3111782250 hasConcept C33923547 @default.
• W3111782250 hasConcept C39432304 @default.
• W3111782250 hasConcept C41008148 @default.
• W3111782250 hasConcept C76886044 @default.
• W3111782250 hasConceptScore W3111782250C105795698 @default.
• W3111782250 hasConceptScore W3111782250C119857082 @default.
• W3111782250 hasConceptScore W3111782250C122058494 @default.
• W3111782250 hasConceptScore W3111782250C127313418 @default.
• W3111782250 hasConceptScore W3111782250C150547873 @default.
• W3111782250 hasConceptScore W3111782250C153294291 @default.
• W3111782250 hasConceptScore W3111782250C187320778 @default.
• W3111782250 hasConceptScore W3111782250C197640229 @default.
• W3111782250 hasConceptScore W3111782250C205649164 @default.
• W3111782250 hasConceptScore W3111782250C2776023875 @default.
• W3111782250 hasConceptScore W3111782250C2776643431 @default.
• W3111782250 hasConceptScore W3111782250C33923547 @default.
• W3111782250 hasConceptScore W3111782250C39432304 @default.
• W3111782250 hasConceptScore W3111782250C41008148 @default.
• W3111782250 hasConceptScore W3111782250C76886044 @default.
• W3111782250 hasFunder F4320321408 @default.
• W3111782250 hasFunder F4320322030 @default.
• W3111782250 hasFunder F4320322120 @default.
• W3111782250 hasLocation W31117822501 @default.
• W3111782250 hasOpenAccess W3111782250 @default.
• W3111782250 hasPrimaryLocation W31117822501 @default.
• W3111782250 hasRelatedWork W1991669790 @default.
• W3111782250 hasRelatedWork W2087579695 @default.
• W3111782250 hasRelatedWork W2093486058 @default.
• W3111782250 hasRelatedWork W213712175 @default.
• W3111782250 hasRelatedWork W2345400589 @default.

• next