FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2893011787> ?p ?o ?g. }

• W2893011787 endingPage "684" @default.
• W2893011787 startingPage "668" @default.
• W2893011787 abstract "Abstract Accurate estimation of pan evaporation (Ep) is required for many applications, e.g., water resources management, irrigation system design and hydrological modeling. However, the estimation of Ep for a target station can be difficult as a result of partial or complete lack of local meteorological data under many conditions. In this study, daily Ep was estimated from local (target-station) and cross-station data in the Poyang Lake Watershed of China using four empirical models and three tree-based machine learning models, including M5 model tree (M5Tree), random forests (RFs) and gradient boosting decision tree (GBDT). Daily meteorological data during 2001–2010 from 16 weather stations were used to train the models, while the data from 2011 to 2015 were used for testing. Two cross-station applications were considered between each of the 16 stations and the other 15 stations. The results showed that the radiation-based Priestley-Taylor model (on average RMSE = 1.13 mm d−1, NSE = 0.53, R2 = 0.57, MBE = 0.21 mm d−1) gave the most accurate daily Ep estimates among the four empirical models during testing, while the mass transfer-based Trabert model (on average RMSE = 1.38 mm d−1, NSE = 0.25, R2 = 0.46, MBE = 0.65 mm d−1) performed worst. The GBDT model outperformed the RFs model, M5Tree model and the empirical models under the same input combinations in terms of prediction accuracy (on average RMSE = 0.86 mm d−1, NSE = 0.68, R2 = 0.73, MBE = 0.07 mm d−1) and model stability (average percentage increase in testing RMSE = 16.3%). The RMSE values generally increased with the increase in the distance of two cross stations. A distance of less than 100 km between two cross stations is highly recommended for cross-station applications with satisfactory prediction accuracy (median percentage increase in RMSE" @default.
• W2893011787 created "2018-10-05" @default.
• W2893011787 creator A5001348758 @default.
• W2893011787 creator A5011930662 @default.
• W2893011787 creator A5042259416 @default.
• W2893011787 creator A5061221648 @default.
• W2893011787 creator A5063213100 @default.
• W2893011787 creator A5082668462 @default.
• W2893011787 date "2018-11-01" @default.
• W2893011787 modified "2023-09-29" @default.
• W2893011787 title "Daily pan evaporation modeling from local and cross-station data using three tree-based machine learning models" @default.
• W2893011787 cites W1109078837 @default.
• W2893011787 cites W1469381315 @default.
• W2893011787 cites W1740585449 @default.
• W2893011787 cites W1964445968 @default.
• W2893011787 cites W1969680722 @default.
• W2893011787 cites W1988984080 @default.
• W2893011787 cites W1992538840 @default.
• W2893011787 cites W2010876855 @default.
• W2893011787 cites W2013355544 @default.
• W2893011787 cites W2017287733 @default.
• W2893011787 cites W2018704760 @default.
• W2893011787 cites W2035018326 @default.
• W2893011787 cites W2039574924 @default.
• W2893011787 cites W2041243118 @default.
• W2893011787 cites W2043360138 @default.
• W2893011787 cites W2051975529 @default.
• W2893011787 cites W2055107850 @default.
• W2893011787 cites W2061819166 @default.
• W2893011787 cites W2064231542 @default.
• W2893011787 cites W2065046554 @default.
• W2893011787 cites W2067973431 @default.
• W2893011787 cites W2070493638 @default.
• W2893011787 cites W2074379462 @default.
• W2893011787 cites W2107956574 @default.
• W2893011787 cites W2115322028 @default.
• W2893011787 cites W2118386433 @default.
• W2893011787 cites W2152236704 @default.
• W2893011787 cites W2160188963 @default.
• W2893011787 cites W2165392588 @default.
• W2893011787 cites W2172396214 @default.
• W2893011787 cites W2260152510 @default.
• W2893011787 cites W2323316069 @default.
• W2893011787 cites W2527066115 @default.
• W2893011787 cites W2548238142 @default.
• W2893011787 cites W2555759795 @default.
• W2893011787 cites W2601379810 @default.
• W2893011787 cites W2603417106 @default.
• W2893011787 cites W2625183221 @default.
• W2893011787 cites W2729015950 @default.
• W2893011787 cites W2733867881 @default.
• W2893011787 cites W2748606210 @default.
• W2893011787 cites W2749106749 @default.
• W2893011787 cites W2767864371 @default.
• W2893011787 cites W2771056109 @default.
• W2893011787 cites W2780554042 @default.
• W2893011787 cites W2791827521 @default.
• W2893011787 cites W2792986592 @default.
• W2893011787 cites W2882203834 @default.
• W2893011787 cites W2889246260 @default.
• W2893011787 cites W3083620949 @default.
• W2893011787 cites W836867855 @default.
• W2893011787 doi "https://doi.org/10.1016/j.jhydrol.2018.09.055" @default.
• W2893011787 hasPublicationYear "2018" @default.
• W2893011787 type Work @default.
• W2893011787 sameAs 2893011787 @default.
• W2893011787 citedByCount "78" @default.
• W2893011787 countsByYear W28930117872019 @default.
• W2893011787 countsByYear W28930117872020 @default.
• W2893011787 countsByYear W28930117872021 @default.
• W2893011787 countsByYear W28930117872022 @default.
• W2893011787 countsByYear W28930117872023 @default.
• W2893011787 crossrefType "journal-article" @default.
• W2893011787 hasAuthorship W2893011787A5001348758 @default.
• W2893011787 hasAuthorship W2893011787A5011930662 @default.
• W2893011787 hasAuthorship W2893011787A5042259416 @default.
• W2893011787 hasAuthorship W2893011787A5061221648 @default.
• W2893011787 hasAuthorship W2893011787A5063213100 @default.
• W2893011787 hasAuthorship W2893011787A5082668462 @default.
• W2893011787 hasConcept C113174947 @default.
• W2893011787 hasConcept C119857082 @default.
• W2893011787 hasConcept C124101348 @default.
• W2893011787 hasConcept C134306372 @default.
• W2893011787 hasConcept C153294291 @default.
• W2893011787 hasConcept C154945302 @default.
• W2893011787 hasConcept C205649164 @default.
• W2893011787 hasConcept C33923547 @default.
• W2893011787 hasConcept C39432304 @default.
• W2893011787 hasConcept C41008148 @default.
• W2893011787 hasConcept C61441594 @default.
• W2893011787 hasConceptScore W2893011787C113174947 @default.
• W2893011787 hasConceptScore W2893011787C119857082 @default.
• W2893011787 hasConceptScore W2893011787C124101348 @default.
• W2893011787 hasConceptScore W2893011787C134306372 @default.
• W2893011787 hasConceptScore W2893011787C153294291 @default.
• W2893011787 hasConceptScore W2893011787C154945302 @default.
• W2893011787 hasConceptScore W2893011787C205649164 @default.
• W2893011787 hasConceptScore W2893011787C33923547 @default.

• next