FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4309483447> ?p ?o ?g. }

• W4309483447 endingPage "105582" @default.
• W4309483447 startingPage "105582" @default.
• W4309483447 abstract "Extreme peak runoff forecasting is still a challenge in hydrology. In fact, the use of traditional physically-based models is limited by the lack of sufficient data and the complexity of the inner hydrological processes. Here, we employ a Machine Learning technique, the Random Forest (RF) together with a combination of Feature Engineering (FE) strategies for adding physical knowledge to RF models and improving their forecasting performances. The FE strategies include precipitation-event classification according to hydrometeorological criteria and separation of flows into baseflow and directflow. We used ∼ 3.5 years of hourly precipitation information retrieved from two near-real-time satellite precipitation databases (PERSIANN-CCS and IMERG-ER), and runoff data at the outlet of a 3391-km 2 basin located in the tropical Andes of Ecuador. The developed models obtained Nash-Sutcliffe efficiencies varying from 0.86 to 0.59 for lead times between 1 to 6 hours. The best performances were obtained for peak runoffs triggered by short-extension precipitation events (<50 km 2 ) where infiltration- or saturation-excess runoff responses are well learned by the RF models. Conversely, the forecasting difficulty is associated with extensive precipitation events. For such conditions, a deeper characterization of the biophysical characteristics of the basin is encouraged for capturing the dynamic of directflow across multiple runoff responses. All in all, the potential to employ near-real-time satellite precipitation and the use of FE strategies for improving RF forecasting provides hydrologists with new tools for real-time runoff forecasting in remote or complex regions. • Near-real-time satellite-precipitation products were tested for peak runoff forecasting. • The addition of feature engineering strategies to machine learning models improved their forecasting efficiencies. • The methods proposed in this study can be used for runoff forecasting in remote or complex areas." @default.
• W4309483447 created "2022-11-28" @default.
• W4309483447 creator A5007686924 @default.
• W4309483447 creator A5015835580 @default.
• W4309483447 creator A5017790998 @default.
• W4309483447 creator A5027772992 @default.
• W4309483447 creator A5073540502 @default.
• W4309483447 date "2023-02-01" @default.
• W4309483447 modified "2023-10-06" @default.
• W4309483447 title "Near-real-time satellite precipitation data ingestion into peak runoff forecasting models" @default.
• W4309483447 cites W1208497260 @default.
• W4309483447 cites W1547333707 @default.
• W4309483447 cites W1986738039 @default.
• W4309483447 cites W1990736903 @default.
• W4309483447 cites W2010248254 @default.
• W4309483447 cites W2015159529 @default.
• W4309483447 cites W2020236162 @default.
• W4309483447 cites W2027550303 @default.
• W4309483447 cites W2033904036 @default.
• W4309483447 cites W2037931255 @default.
• W4309483447 cites W2048500910 @default.
• W4309483447 cites W2078690500 @default.
• W4309483447 cites W2081062237 @default.
• W4309483447 cites W2089311956 @default.
• W4309483447 cites W2093827913 @default.
• W4309483447 cites W2094653192 @default.
• W4309483447 cites W2101108516 @default.
• W4309483447 cites W2119803205 @default.
• W4309483447 cites W2130506148 @default.
• W4309483447 cites W2138763184 @default.
• W4309483447 cites W2148615810 @default.
• W4309483447 cites W2151071504 @default.
• W4309483447 cites W2166210698 @default.
• W4309483447 cites W2170100555 @default.
• W4309483447 cites W2185358055 @default.
• W4309483447 cites W2216946510 @default.
• W4309483447 cites W2472201467 @default.
• W4309483447 cites W2496120601 @default.
• W4309483447 cites W2519400983 @default.
• W4309483447 cites W2531233546 @default.
• W4309483447 cites W2570501755 @default.
• W4309483447 cites W2572622164 @default.
• W4309483447 cites W2791152274 @default.
• W4309483447 cites W2815792770 @default.
• W4309483447 cites W2872938893 @default.
• W4309483447 cites W2889514903 @default.
• W4309483447 cites W2903988118 @default.
• W4309483447 cites W2911964244 @default.
• W4309483447 cites W2913066849 @default.
• W4309483447 cites W3008241583 @default.
• W4309483447 cites W3036238619 @default.
• W4309483447 cites W3037747661 @default.
• W4309483447 cites W3088869868 @default.
• W4309483447 cites W3127661493 @default.
• W4309483447 cites W3164951075 @default.
• W4309483447 cites W3177321656 @default.
• W4309483447 cites W3198665548 @default.
• W4309483447 cites W4281612463 @default.
• W4309483447 cites W588320544 @default.
• W4309483447 doi "https://doi.org/10.1016/j.envsoft.2022.105582" @default.
• W4309483447 hasPublicationYear "2023" @default.
• W4309483447 type Work @default.
• W4309483447 citedByCount "2" @default.
• W4309483447 countsByYear W43094834472023 @default.
• W4309483447 crossrefType "journal-article" @default.
• W4309483447 hasAuthorship W4309483447A5007686924 @default.
• W4309483447 hasAuthorship W4309483447A5015835580 @default.
• W4309483447 hasAuthorship W4309483447A5017790998 @default.
• W4309483447 hasAuthorship W4309483447A5027772992 @default.
• W4309483447 hasAuthorship W4309483447A5073540502 @default.
• W4309483447 hasBestOaLocation W43094834472 @default.
• W4309483447 hasConcept C107054158 @default.
• W4309483447 hasConcept C126322002 @default.
• W4309483447 hasConcept C127313418 @default.
• W4309483447 hasConcept C127413603 @default.
• W4309483447 hasConcept C146978453 @default.
• W4309483447 hasConcept C153294291 @default.
• W4309483447 hasConcept C18903297 @default.
• W4309483447 hasConcept C19269812 @default.
• W4309483447 hasConcept C193230392 @default.
• W4309483447 hasConcept C205649164 @default.
• W4309483447 hasConcept C39432304 @default.
• W4309483447 hasConcept C49204034 @default.
• W4309483447 hasConcept C50477045 @default.
• W4309483447 hasConcept C71924100 @default.
• W4309483447 hasConcept C86803240 @default.
• W4309483447 hasConceptScore W4309483447C107054158 @default.
• W4309483447 hasConceptScore W4309483447C126322002 @default.
• W4309483447 hasConceptScore W4309483447C127313418 @default.
• W4309483447 hasConceptScore W4309483447C127413603 @default.
• W4309483447 hasConceptScore W4309483447C146978453 @default.
• W4309483447 hasConceptScore W4309483447C153294291 @default.
• W4309483447 hasConceptScore W4309483447C18903297 @default.
• W4309483447 hasConceptScore W4309483447C19269812 @default.
• W4309483447 hasConceptScore W4309483447C193230392 @default.
• W4309483447 hasConceptScore W4309483447C205649164 @default.
• W4309483447 hasConceptScore W4309483447C39432304 @default.
• W4309483447 hasConceptScore W4309483447C49204034 @default.

• next