Matches in SemOpenAlex for { <https://semopenalex.org/work/W3203298825> ?p ?o ?g. }
Showing items 1 to 76 of
76
with 100 items per page.
- W3203298825 abstract "Abstract Despite advances in artificial intelligence modelling, the lack of soil erosion data and other watershed information is still one of the important factors limiting soil-erosion modelling. Additionally, the limited number of parameters and the lack of evaluation criteria are major disadvantages of empirical soil-erosion models. To overcome these limitations, we introduce a new approach that integrates empirical and artificial intelligence models. Erosion-prone locations (erosion ≥16 tons/ha/year) are identified using RUSLE model and a soil-erosion map is prepared using random forest (RF), artificial neural network (ANN), classification tree analysis (CTA), and generalized linear model (GLM). This study uses 13 factors affecting soil erosion in the Talar watershed, Iran, to increase prediction accuracy. The results reveal that the RF model has the highest prediction performance (AUC=0.95, Kappa=0.87, Accuracy=0.93, and Bias=0.88), outperforming the three machine-learning models. The results show that slope angle, land use/land cover, elevation, and rainfall erosivity are the factors that contribute the most to soil erosion propensity in the watershed. Curvature and topography position index (TPI) were removed from the analysis due to multicollinearity with other factors. The results can be used to improve the identification of hot spots of soil erosion, especially in watersheds for which soil-erosion data are limited." @default.
- W3203298825 created "2021-10-11" @default.
- W3203298825 creator A5042743096 @default.
- W3203298825 creator A5062128630 @default.
- W3203298825 creator A5065823750 @default.
- W3203298825 creator A5087279621 @default.
- W3203298825 creator A5088130162 @default.
- W3203298825 date "2021-09-29" @default.
- W3203298825 modified "2023-09-27" @default.
- W3203298825 title "A New Approach For Smart Soil Erosion Modelling: Integration of Empirical And Machine Learning Models" @default.
- W3203298825 doi "https://doi.org/10.21203/rs.3.rs-809330/v1" @default.
- W3203298825 hasPublicationYear "2021" @default.
- W3203298825 type Work @default.
- W3203298825 sameAs 3203298825 @default.
- W3203298825 citedByCount "1" @default.
- W3203298825 countsByYear W32032988252022 @default.
- W3203298825 crossrefType "posted-content" @default.
- W3203298825 hasAuthorship W3203298825A5042743096 @default.
- W3203298825 hasAuthorship W3203298825A5062128630 @default.
- W3203298825 hasAuthorship W3203298825A5065823750 @default.
- W3203298825 hasAuthorship W3203298825A5087279621 @default.
- W3203298825 hasAuthorship W3203298825A5088130162 @default.
- W3203298825 hasBestOaLocation W32032988251 @default.
- W3203298825 hasConcept C114793014 @default.
- W3203298825 hasConcept C119857082 @default.
- W3203298825 hasConcept C123157820 @default.
- W3203298825 hasConcept C127313418 @default.
- W3203298825 hasConcept C133199616 @default.
- W3203298825 hasConcept C150547873 @default.
- W3203298825 hasConcept C152877465 @default.
- W3203298825 hasConcept C159390177 @default.
- W3203298825 hasConcept C169258074 @default.
- W3203298825 hasConcept C181843262 @default.
- W3203298825 hasConcept C187320778 @default.
- W3203298825 hasConcept C189285262 @default.
- W3203298825 hasConcept C39432304 @default.
- W3203298825 hasConcept C41008148 @default.
- W3203298825 hasConcept C44154836 @default.
- W3203298825 hasConcept C45804977 @default.
- W3203298825 hasConcept C62649853 @default.
- W3203298825 hasConcept C76886044 @default.
- W3203298825 hasConceptScore W3203298825C114793014 @default.
- W3203298825 hasConceptScore W3203298825C119857082 @default.
- W3203298825 hasConceptScore W3203298825C123157820 @default.
- W3203298825 hasConceptScore W3203298825C127313418 @default.
- W3203298825 hasConceptScore W3203298825C133199616 @default.
- W3203298825 hasConceptScore W3203298825C150547873 @default.
- W3203298825 hasConceptScore W3203298825C152877465 @default.
- W3203298825 hasConceptScore W3203298825C159390177 @default.
- W3203298825 hasConceptScore W3203298825C169258074 @default.
- W3203298825 hasConceptScore W3203298825C181843262 @default.
- W3203298825 hasConceptScore W3203298825C187320778 @default.
- W3203298825 hasConceptScore W3203298825C189285262 @default.
- W3203298825 hasConceptScore W3203298825C39432304 @default.
- W3203298825 hasConceptScore W3203298825C41008148 @default.
- W3203298825 hasConceptScore W3203298825C44154836 @default.
- W3203298825 hasConceptScore W3203298825C45804977 @default.
- W3203298825 hasConceptScore W3203298825C62649853 @default.
- W3203298825 hasConceptScore W3203298825C76886044 @default.
- W3203298825 hasLocation W32032988251 @default.
- W3203298825 hasOpenAccess W3203298825 @default.
- W3203298825 hasPrimaryLocation W32032988251 @default.
- W3203298825 hasRelatedWork W2054633499 @default.
- W3203298825 hasRelatedWork W2357103096 @default.
- W3203298825 hasRelatedWork W2381047063 @default.
- W3203298825 hasRelatedWork W2382634454 @default.
- W3203298825 hasRelatedWork W2386108390 @default.
- W3203298825 hasRelatedWork W2969111275 @default.
- W3203298825 hasRelatedWork W3202554822 @default.
- W3203298825 hasRelatedWork W3203298825 @default.
- W3203298825 hasRelatedWork W2110007881 @default.
- W3203298825 hasRelatedWork W2187372479 @default.
- W3203298825 isParatext "false" @default.
- W3203298825 isRetracted "false" @default.
- W3203298825 magId "3203298825" @default.
- W3203298825 workType "article" @default.