FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3108802587> ?p ?o ?g. }

• W3108802587 abstract "Information about future streamflow is important for hydropower production planning, especially for damless hydro-power plants. The river flow is a reflection of various hydrological, hydrogeological, and meteorological factors, which increases the direct modeling difficulty, and favors the use of data-driven methods. In this paper, we propose the use of one-dimensional convolutional neural networks (1d-CNN) for multi-day ahead river flow forecasting and we present a multi-input model using correlated-input time-series. The proposed model was applied at the Madeira River, the Amazon’s largest and most important tributary, near the Santo Antonio damless hydro-power plant. We compared the proposed correlated-input 1d-CNN to a single-input 1d-CNN model and some baseline models. Furthermore, we conclude that 1d-CNN performed better than all baseline models and that the correlated-input forecasting model is 5 times smaller than the single-input equivalent with accuracy improvements." @default.
• W3108802587 created "2020-12-07" @default.
• W3108802587 creator A5010262875 @default.
• W3108802587 creator A5037165857 @default.
• W3108802587 creator A5037876987 @default.
• W3108802587 creator A5064063675 @default.
• W3108802587 creator A5080284280 @default.
• W3108802587 date "2020-01-01" @default.
• W3108802587 modified "2023-10-17" @default.
• W3108802587 title "Correlated Time-Series in Multi-Day-Ahead Streamflow Forecasting Using Convolutional Networks" @default.
• W3108802587 cites W1589475696 @default.
• W3108802587 cites W1965117094 @default.
• W3108802587 cites W1975938969 @default.
• W3108802587 cites W1986143844 @default.
• W3108802587 cites W1989543665 @default.
• W3108802587 cites W1992150403 @default.
• W3108802587 cites W1997016663 @default.
• W3108802587 cites W2004796972 @default.
• W3108802587 cites W2018767964 @default.
• W3108802587 cites W2023535140 @default.
• W3108802587 cites W2035902791 @default.
• W3108802587 cites W2057433062 @default.
• W3108802587 cites W2071272611 @default.
• W3108802587 cites W2072128103 @default.
• W3108802587 cites W2074993972 @default.
• W3108802587 cites W2092514020 @default.
• W3108802587 cites W2101706954 @default.
• W3108802587 cites W2110242546 @default.
• W3108802587 cites W2123585936 @default.
• W3108802587 cites W2126865282 @default.
• W3108802587 cites W2129462366 @default.
• W3108802587 cites W2135803899 @default.
• W3108802587 cites W2140397276 @default.
• W3108802587 cites W2146059660 @default.
• W3108802587 cites W2148391762 @default.
• W3108802587 cites W2154579312 @default.
• W3108802587 cites W2159164703 @default.
• W3108802587 cites W2175514636 @default.
• W3108802587 cites W2288074780 @default.
• W3108802587 cites W2301486910 @default.
• W3108802587 cites W2561043568 @default.
• W3108802587 cites W2735895797 @default.
• W3108802587 cites W2742473260 @default.
• W3108802587 cites W2748304916 @default.
• W3108802587 cites W2759690896 @default.
• W3108802587 cites W2774966631 @default.
• W3108802587 cites W2796601240 @default.
• W3108802587 cites W2799918535 @default.
• W3108802587 cites W2815792770 @default.
• W3108802587 cites W2885643851 @default.
• W3108802587 cites W2895135437 @default.
• W3108802587 cites W2895979207 @default.
• W3108802587 cites W2902644322 @default.
• W3108802587 cites W2903265999 @default.
• W3108802587 cites W2908827354 @default.
• W3108802587 cites W2955613755 @default.
• W3108802587 cites W2964121744 @default.
• W3108802587 cites W2965201272 @default.
• W3108802587 cites W2968912269 @default.
• W3108802587 cites W3003975888 @default.
• W3108802587 cites W3004852406 @default.
• W3108802587 cites W3014708634 @default.
• W3108802587 cites W3146803896 @default.
• W3108802587 cites W841982912 @default.
• W3108802587 doi "https://doi.org/10.1109/access.2020.3040942" @default.
• W3108802587 hasPublicationYear "2020" @default.
• W3108802587 type Work @default.
• W3108802587 sameAs 3108802587 @default.
• W3108802587 citedByCount "7" @default.
• W3108802587 countsByYear W31088025872021 @default.
• W3108802587 countsByYear W31088025872022 @default.
• W3108802587 countsByYear W31088025872023 @default.
• W3108802587 crossrefType "journal-article" @default.
• W3108802587 hasAuthorship W3108802587A5010262875 @default.
• W3108802587 hasAuthorship W3108802587A5037165857 @default.
• W3108802587 hasAuthorship W3108802587A5037876987 @default.
• W3108802587 hasAuthorship W3108802587A5064063675 @default.
• W3108802587 hasAuthorship W3108802587A5080284280 @default.
• W3108802587 hasBestOaLocation W31088025871 @default.
• W3108802587 hasConcept C111368507 @default.
• W3108802587 hasConcept C119599485 @default.
• W3108802587 hasConcept C119857082 @default.
• W3108802587 hasConcept C124101348 @default.
• W3108802587 hasConcept C126645576 @default.
• W3108802587 hasConcept C12725497 @default.
• W3108802587 hasConcept C127313418 @default.
• W3108802587 hasConcept C127413603 @default.
• W3108802587 hasConcept C143724316 @default.
• W3108802587 hasConcept C151406439 @default.
• W3108802587 hasConcept C151730666 @default.
• W3108802587 hasConcept C154945302 @default.
• W3108802587 hasConcept C16828302 @default.
• W3108802587 hasConcept C187320778 @default.
• W3108802587 hasConcept C205649164 @default.
• W3108802587 hasConcept C33556824 @default.
• W3108802587 hasConcept C40675005 @default.
• W3108802587 hasConcept C41008148 @default.
• W3108802587 hasConcept C53739315 @default.
• W3108802587 hasConcept C58640448 @default.
• W3108802587 hasConcept C76886044 @default.

• next