FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2038281504> ?p ?o ?g. }

• W2038281504 endingPage "315" @default.
• W2038281504 startingPage "299" @default.
• W2038281504 abstract "Predictive models are increasingly used in geomorphology, but systematic evaluations of novel statistical techniques are still limited. The aim of this study was to compare the accuracy of generalized linear models (GLM), generalized additive models (GAM), classification tree analysis (CTA), neural networks (ANN) and multiple adaptive regression splines (MARS) in predictive geomorphological modelling. Five different distribution models both for non-sorted and sorted patterned ground were constructed on the basis of four terrain parameters and four soil variables. To evaluate the models, the original data set of 9997 squares of 1 ha in size was randomly divided into model training (70%, n=6998) and model evaluation sets (30%, n=2999). In general, active sorted patterned ground is clearly defined in upper fell areas with high slope angle and till soils. Active non-sorted patterned ground is more common in valleys with higher soil moisture and fine-scale concave topography. The predictive performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC) and the Kappa value. The relatively high discrimination capacity of all models, AUC=0.85–0.88 and Kappa=0.49–0.56, implies that the model's predictions provide an acceptable index of sorted and non-sorted patterned ground occurrence. The best performance for model calibration data for both data sets was achieved by the CTA. However, when the predictive mapping ability was explored through the evaluation data set, the model accuracies of CTA decreased clearly compared to the other modelling techniques. For model evaluation data MARS performed marginally best. Our results show that the digital elevation model and soil data can be used to predict relatively robustly the activity of patterned ground in fine scale in a subarctic landscape. This indicates that predictive geomorphological modelling has the advantage of providing relevant and useful information on earth surface processes over extensive areas, such data being unavailable through more conventional survey methods." @default.
• W2038281504 created "2016-06-24" @default.
• W2038281504 creator A5062381603 @default.
• W2038281504 creator A5080656961 @default.
• W2038281504 date "2005-04-01" @default.
• W2038281504 modified "2023-09-25" @default.
• W2038281504 title "Evaluation of current statistical approaches for predictive geomorphological mapping" @default.
• W2038281504 cites W1583497136 @default.
• W2038281504 cites W1622804157 @default.
• W2038281504 cites W1969032991 @default.
• W2038281504 cites W1970714669 @default.
• W2038281504 cites W1972729541 @default.
• W2038281504 cites W1975582222 @default.
• W2038281504 cites W1981646498 @default.
• W2038281504 cites W1985414380 @default.
• W2038281504 cites W1990748933 @default.
• W2038281504 cites W1993388553 @default.
• W2038281504 cites W1999659160 @default.
• W2038281504 cites W2000532370 @default.
• W2038281504 cites W2004447223 @default.
• W2038281504 cites W2032759423 @default.
• W2038281504 cites W2039875746 @default.
• W2038281504 cites W2042166689 @default.
• W2038281504 cites W2047813444 @default.
• W2038281504 cites W2060298585 @default.
• W2038281504 cites W2070262419 @default.
• W2038281504 cites W2077573539 @default.
• W2038281504 cites W2081964130 @default.
• W2038281504 cites W2084113824 @default.
• W2038281504 cites W2086431105 @default.
• W2038281504 cites W2089454337 @default.
• W2038281504 cites W2089792340 @default.
• W2038281504 cites W2093635393 @default.
• W2038281504 cites W2102201073 @default.
• W2038281504 cites W2106478824 @default.
• W2038281504 cites W2107844016 @default.
• W2038281504 cites W2113746882 @default.
• W2038281504 cites W2115268776 @default.
• W2038281504 cites W2120160157 @default.
• W2038281504 cites W2121795775 @default.
• W2038281504 cites W2124118467 @default.
• W2038281504 cites W2138973222 @default.
• W2038281504 cites W2138988968 @default.
• W2038281504 cites W2139369379 @default.
• W2038281504 cites W2139857790 @default.
• W2038281504 cites W2147555471 @default.
• W2038281504 cites W2149808755 @default.
• W2038281504 cites W2156319159 @default.
• W2038281504 cites W2159398439 @default.
• W2038281504 cites W2164777277 @default.
• W2038281504 cites W2167827350 @default.
• W2038281504 cites W2168651768 @default.
• W2038281504 cites W2172009270 @default.
• W2038281504 cites W2746680544 @default.
• W2038281504 cites W2748437574 @default.
• W2038281504 cites W4243871771 @default.
• W2038281504 cites W4253772628 @default.
• W2038281504 cites W4254900611 @default.
• W2038281504 doi "https://doi.org/10.1016/j.geomorph.2004.10.006" @default.
• W2038281504 hasPublicationYear "2005" @default.
• W2038281504 type Work @default.
• W2038281504 sameAs 2038281504 @default.
• W2038281504 citedByCount "94" @default.
• W2038281504 countsByYear W20382815042012 @default.
• W2038281504 countsByYear W20382815042013 @default.
• W2038281504 countsByYear W20382815042014 @default.
• W2038281504 countsByYear W20382815042015 @default.
• W2038281504 countsByYear W20382815042016 @default.
• W2038281504 countsByYear W20382815042017 @default.
• W2038281504 countsByYear W20382815042018 @default.
• W2038281504 countsByYear W20382815042019 @default.
• W2038281504 countsByYear W20382815042020 @default.
• W2038281504 countsByYear W20382815042021 @default.
• W2038281504 countsByYear W20382815042022 @default.
• W2038281504 countsByYear W20382815042023 @default.
• W2038281504 crossrefType "journal-article" @default.
• W2038281504 hasAuthorship W2038281504A5062381603 @default.
• W2038281504 hasAuthorship W2038281504A5080656961 @default.
• W2038281504 hasConcept C105795698 @default.
• W2038281504 hasConcept C120068334 @default.
• W2038281504 hasConcept C121332964 @default.
• W2038281504 hasConcept C127313418 @default.
• W2038281504 hasConcept C1276947 @default.
• W2038281504 hasConcept C161840515 @default.
• W2038281504 hasConcept C165838908 @default.
• W2038281504 hasConcept C205649164 @default.
• W2038281504 hasConcept C2524010 @default.
• W2038281504 hasConcept C2778724333 @default.
• W2038281504 hasConcept C2778755073 @default.
• W2038281504 hasConcept C33923547 @default.
• W2038281504 hasConcept C44882253 @default.
• W2038281504 hasConcept C48921125 @default.
• W2038281504 hasConcept C58489278 @default.
• W2038281504 hasConcept C58640448 @default.
• W2038281504 hasConcept C83260615 @default.
• W2038281504 hasConceptScore W2038281504C105795698 @default.
• W2038281504 hasConceptScore W2038281504C120068334 @default.
• W2038281504 hasConceptScore W2038281504C121332964 @default.

• next