Matches in SemOpenAlex for { <https://semopenalex.org/work/W4285039332> ?p ?o ?g. }
- W4285039332 endingPage "1754" @default.
- W4285039332 startingPage "1740" @default.
- W4285039332 abstract "This study aims to predict channel unit types (CUTs) by combining remotely sensed data with morphological variables using machine learning algorithms (random forest, support vector machines, multiple adaptive regression splines, extreme gradient boosting and adaptive boosting) within the Upper Ogun River Basin, Southwestern Nigeria. In achieving the aim of this study, we identified the most important variable(s) in CUT discrimination using the random forest – recursive feature elimination (RF-RFE). A total of 249 cross-sections across 83 reaches were sampled during the fieldwork. Landsat 8 and Sentinel-1 bands were retrieved for days the fieldwork was carried and mosaiced using the Google Earth Engine platform. The RF-RFE identified five top variables (accuracy: 0.79 ± 0.14; kappa: 0.39) discriminating the CUT as dimensionless stream power, slope, width, wetted perimeter and Band 4. In essence, there is much hope in the use of remote sensing in CUT mapping at the reach scale." @default.
- W4285039332 created "2022-07-12" @default.
- W4285039332 creator A5008907119 @default.
- W4285039332 creator A5019100992 @default.
- W4285039332 creator A5071518515 @default.
- W4285039332 creator A5084164098 @default.
- W4285039332 date "2022-08-15" @default.
- W4285039332 modified "2023-10-17" @default.
- W4285039332 title "River sensing: the inclusion of red band in predicting reach-scale types using machine learning algorithms" @default.
- W4285039332 cites W1513248693 @default.
- W4285039332 cites W1565724073 @default.
- W4285039332 cites W1831050183 @default.
- W4285039332 cites W1937979409 @default.
- W4285039332 cites W1948053546 @default.
- W4285039332 cites W1971900122 @default.
- W4285039332 cites W1972670970 @default.
- W4285039332 cites W1981745585 @default.
- W4285039332 cites W1993737188 @default.
- W4285039332 cites W2007885033 @default.
- W4285039332 cites W2008056655 @default.
- W4285039332 cites W2009396719 @default.
- W4285039332 cites W2009775537 @default.
- W4285039332 cites W2015451330 @default.
- W4285039332 cites W2020925934 @default.
- W4285039332 cites W2031215712 @default.
- W4285039332 cites W2037923151 @default.
- W4285039332 cites W2053055449 @default.
- W4285039332 cites W2055685380 @default.
- W4285039332 cites W2061914688 @default.
- W4285039332 cites W2081620141 @default.
- W4285039332 cites W2088349978 @default.
- W4285039332 cites W2096701663 @default.
- W4285039332 cites W2102201073 @default.
- W4285039332 cites W2113974897 @default.
- W4285039332 cites W2116513335 @default.
- W4285039332 cites W2116935344 @default.
- W4285039332 cites W2129118762 @default.
- W4285039332 cites W2129986698 @default.
- W4285039332 cites W2133404858 @default.
- W4285039332 cites W2136936714 @default.
- W4285039332 cites W2161087344 @default.
- W4285039332 cites W2168299194 @default.
- W4285039332 cites W2169938632 @default.
- W4285039332 cites W2216444323 @default.
- W4285039332 cites W2287863848 @default.
- W4285039332 cites W2344328155 @default.
- W4285039332 cites W2476654748 @default.
- W4285039332 cites W2505328891 @default.
- W4285039332 cites W2612350949 @default.
- W4285039332 cites W2737548078 @default.
- W4285039332 cites W2767785952 @default.
- W4285039332 cites W2800341502 @default.
- W4285039332 cites W2810451272 @default.
- W4285039332 cites W2911964244 @default.
- W4285039332 cites W2994984469 @default.
- W4285039332 cites W3004888958 @default.
- W4285039332 cites W3006436046 @default.
- W4285039332 cites W3013767593 @default.
- W4285039332 cites W3016199669 @default.
- W4285039332 cites W3020629826 @default.
- W4285039332 cites W3027915818 @default.
- W4285039332 cites W3099079911 @default.
- W4285039332 cites W3102476541 @default.
- W4285039332 cites W3123619755 @default.
- W4285039332 cites W3156415921 @default.
- W4285039332 cites W3182056459 @default.
- W4285039332 cites W4212883601 @default.
- W4285039332 doi "https://doi.org/10.1080/02626667.2022.2098752" @default.
- W4285039332 hasPublicationYear "2022" @default.
- W4285039332 type Work @default.
- W4285039332 citedByCount "2" @default.
- W4285039332 countsByYear W42850393322022 @default.
- W4285039332 countsByYear W42850393322023 @default.
- W4285039332 crossrefType "journal-article" @default.
- W4285039332 hasAuthorship W4285039332A5008907119 @default.
- W4285039332 hasAuthorship W4285039332A5019100992 @default.
- W4285039332 hasAuthorship W4285039332A5071518515 @default.
- W4285039332 hasAuthorship W4285039332A5084164098 @default.
- W4285039332 hasConcept C105795698 @default.
- W4285039332 hasConcept C11413529 @default.
- W4285039332 hasConcept C119857082 @default.
- W4285039332 hasConcept C121332964 @default.
- W4285039332 hasConcept C12267149 @default.
- W4285039332 hasConcept C127313418 @default.
- W4285039332 hasConcept C154945302 @default.
- W4285039332 hasConcept C169258074 @default.
- W4285039332 hasConcept C205649164 @default.
- W4285039332 hasConcept C24872484 @default.
- W4285039332 hasConcept C2778755073 @default.
- W4285039332 hasConcept C33923547 @default.
- W4285039332 hasConcept C41008148 @default.
- W4285039332 hasConcept C46686674 @default.
- W4285039332 hasConcept C57879066 @default.
- W4285039332 hasConcept C58640448 @default.
- W4285039332 hasConcept C62649853 @default.
- W4285039332 hasConcept C70153297 @default.
- W4285039332 hasConcept C83546350 @default.
- W4285039332 hasConceptScore W4285039332C105795698 @default.