FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4321227761> ?p ?o ?g. }

• W4321227761 endingPage "52" @default.
• W4321227761 startingPage "52" @default.
• W4321227761 abstract "To estimate rainfall from remote sensing data, three machine learning-based regression models, K-Nearest Neighbors Regression (K-NNR), Support Vector Regression (SVR), and Random Forest Regression (RFR), were implemented using MSG (Meteosat Second Generation) satellite data. Daytime and nighttime data from a rain gauge are used for model training and validation. To optimize the results, the outputs of the three models are combined using the weighted average. The combination of the three models (hereafter called Com-RSK) markedly improved the predictions. Indeed, the MAE, MBE, RMSE and correlation coefficient went from 23.6 mm, 10.0 mm, 40.6 mm and 89% for the SVR to 20.7 mm, 5.5 mm, 37.4 mm, and 94% when the models were combined, respectively. The Com-RSK is also compared to a few methods using the classification in the estimation, such as the ECST Enhanced Convective Stratiform Technique (ECST), the MMultic technique, and the Convective/Stratiform Rain Area Delineation Technique (CS-RADT). The Com-RSK show superior performance compared to ECST, MMultic and CS-RADT methods.The Com-RSK is also compared to the two products of satellite estimates, namely CMORPH and CHIRPS. The results indicate that Com-RSK performs better than CMORPH and CHIRPS according to MBE, RMSE and CC (coefficient correlation). A comparison with three types of satellite precipitation estimation products, such as global product, regional product, and near real-time product, is performed. Overall, the methodology developed here shows almost the same results as regional product methods and exhibits better results than near real-time and global product methods." @default.
• W4321227761 created "2023-02-18" @default.
• W4321227761 creator A5002001302 @default.
• W4321227761 creator A5032336922 @default.
• W4321227761 creator A5046812686 @default.
• W4321227761 creator A5058635485 @default.
• W4321227761 creator A5060909895 @default.
• W4321227761 creator A5068179834 @default.
• W4321227761 date "2023-02-16" @default.
• W4321227761 modified "2023-09-25" @default.
• W4321227761 title "Quantitative Estimation of Rainfall from Remote Sensing Data Using Machine Learning Regression Models" @default.
• W4321227761 cites W1998821638 @default.
• W4321227761 cites W2055308682 @default.
• W4321227761 cites W2069229431 @default.
• W4321227761 cites W2077992157 @default.
• W4321227761 cites W2082027031 @default.
• W4321227761 cites W2089848249 @default.
• W4321227761 cites W2094411614 @default.
• W4321227761 cites W2122111042 @default.
• W4321227761 cites W2145458969 @default.
• W4321227761 cites W2156909104 @default.
• W4321227761 cites W2158830316 @default.
• W4321227761 cites W2340938351 @default.
• W4321227761 cites W2463308442 @default.
• W4321227761 cites W2560205687 @default.
• W4321227761 cites W2560268323 @default.
• W4321227761 cites W2585958134 @default.
• W4321227761 cites W2805001781 @default.
• W4321227761 cites W2906208963 @default.
• W4321227761 cites W2911964244 @default.
• W4321227761 cites W2952646973 @default.
• W4321227761 cites W2966888951 @default.
• W4321227761 cites W2978037545 @default.
• W4321227761 cites W2991131599 @default.
• W4321227761 cites W2991648504 @default.
• W4321227761 cites W2998684305 @default.
• W4321227761 cites W3006909998 @default.
• W4321227761 cites W3037705974 @default.
• W4321227761 cites W3047604526 @default.
• W4321227761 cites W3096052669 @default.
• W4321227761 cites W3130269368 @default.
• W4321227761 cites W3158124256 @default.
• W4321227761 cites W3198350258 @default.
• W4321227761 cites W3216924497 @default.
• W4321227761 cites W4205111810 @default.
• W4321227761 cites W4220674835 @default.
• W4321227761 cites W4294631258 @default.
• W4321227761 doi "https://doi.org/10.3390/hydrology10020052" @default.
• W4321227761 hasPublicationYear "2023" @default.
• W4321227761 type Work @default.
• W4321227761 citedByCount "0" @default.
• W4321227761 crossrefType "journal-article" @default.
• W4321227761 hasAuthorship W4321227761A5002001302 @default.
• W4321227761 hasAuthorship W4321227761A5032336922 @default.
• W4321227761 hasAuthorship W4321227761A5046812686 @default.
• W4321227761 hasAuthorship W4321227761A5058635485 @default.
• W4321227761 hasAuthorship W4321227761A5060909895 @default.
• W4321227761 hasAuthorship W4321227761A5068179834 @default.
• W4321227761 hasBestOaLocation W43212277611 @default.
• W4321227761 hasConcept C105795698 @default.
• W4321227761 hasConcept C107054158 @default.
• W4321227761 hasConcept C119857082 @default.
• W4321227761 hasConcept C120961793 @default.
• W4321227761 hasConcept C12267149 @default.
• W4321227761 hasConcept C127413603 @default.
• W4321227761 hasConcept C139945424 @default.
• W4321227761 hasConcept C146978453 @default.
• W4321227761 hasConcept C152877465 @default.
• W4321227761 hasConcept C153294291 @default.
• W4321227761 hasConcept C169258074 @default.
• W4321227761 hasConcept C19269812 @default.
• W4321227761 hasConcept C205649164 @default.
• W4321227761 hasConcept C27181475 @default.
• W4321227761 hasConcept C2780092901 @default.
• W4321227761 hasConcept C33923547 @default.
• W4321227761 hasConcept C39432304 @default.
• W4321227761 hasConcept C41008148 @default.
• W4321227761 hasConcept C62649853 @default.
• W4321227761 hasConcept C83546350 @default.
• W4321227761 hasConceptScore W4321227761C105795698 @default.
• W4321227761 hasConceptScore W4321227761C107054158 @default.
• W4321227761 hasConceptScore W4321227761C119857082 @default.
• W4321227761 hasConceptScore W4321227761C120961793 @default.
• W4321227761 hasConceptScore W4321227761C12267149 @default.
• W4321227761 hasConceptScore W4321227761C127413603 @default.
• W4321227761 hasConceptScore W4321227761C139945424 @default.
• W4321227761 hasConceptScore W4321227761C146978453 @default.
• W4321227761 hasConceptScore W4321227761C152877465 @default.
• W4321227761 hasConceptScore W4321227761C153294291 @default.
• W4321227761 hasConceptScore W4321227761C169258074 @default.
• W4321227761 hasConceptScore W4321227761C19269812 @default.
• W4321227761 hasConceptScore W4321227761C205649164 @default.
• W4321227761 hasConceptScore W4321227761C27181475 @default.
• W4321227761 hasConceptScore W4321227761C2780092901 @default.
• W4321227761 hasConceptScore W4321227761C33923547 @default.
• W4321227761 hasConceptScore W4321227761C39432304 @default.
• W4321227761 hasConceptScore W4321227761C41008148 @default.
• W4321227761 hasConceptScore W4321227761C62649853 @default.

• next