FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4321597578> ?p ?o ?g. }

• W4321597578 endingPage "856" @default.
• W4321597578 startingPage "856" @default.
• W4321597578 abstract "Mismanagement of fresh water is a primary concern that negatively impacts agricultural productivity. Judicious use of water in agriculture is possible by estimating the optimal requirement. The present practice of estimating crop water requirements is using reference evapotranspiration (ET0) values, which is considered a standard method. Hence, predicting ET0 is vital in allocating and managing available resources. In this study, different machine learning (ML) algorithms, namely random forests (RF), extreme gradient boosting (XGB), and light gradient boosting (LGB), were optimized using the naturally inspired grey wolf optimizer (GWO) viz. GWORF, GWOXGB, and GWOLGB. The daily meteorological data of 10 locations falling under humid and sub-humid regions of India for different cross-validation stages were employed, using eighteen input scenarios. Besides, different empirical models were also compared with the ML models. The hybrid ML models were found superior in accurately predicting at all the stations than the conventional and empirical models. The reduction in the root mean square error (RMSE) from 0.919 to 0.812 mm/day in the humid region and 1.253 mm/day to 1.154 mm/day in the sub-humid region was seen in the least accurate model using the hyperparameter tuning. The RF models have improved their accuracies substantially using the GWO optimizer than LGB and XGB models." @default.
• W4321597578 created "2023-02-24" @default.
• W4321597578 creator A5036553945 @default.
• W4321597578 creator A5043096945 @default.
• W4321597578 creator A5058514086 @default.
• W4321597578 creator A5085365196 @default.
• W4321597578 date "2023-02-22" @default.
• W4321597578 modified "2023-09-30" @default.
• W4321597578 title "Predictive Modelling of Reference Evapotranspiration Using Machine Learning Models Coupled with Grey Wolf Optimizer" @default.
• W4321597578 cites W1469381315 @default.
• W4321597578 cites W1542005766 @default.
• W4321597578 cites W1678356000 @default.
• W4321597578 cites W1992795193 @default.
• W4321597578 cites W2003696872 @default.
• W4321597578 cites W2011642135 @default.
• W4321597578 cites W2040097802 @default.
• W4321597578 cites W2054440616 @default.
• W4321597578 cites W2057543147 @default.
• W4321597578 cites W2061438946 @default.
• W4321597578 cites W2063757041 @default.
• W4321597578 cites W2085731133 @default.
• W4321597578 cites W2088456721 @default.
• W4321597578 cites W2136419133 @default.
• W4321597578 cites W2149409317 @default.
• W4321597578 cites W2153015843 @default.
• W4321597578 cites W2172396214 @default.
• W4321597578 cites W2181201162 @default.
• W4321597578 cites W2273916808 @default.
• W4321597578 cites W2281831809 @default.
• W4321597578 cites W2296778215 @default.
• W4321597578 cites W2321683632 @default.
• W4321597578 cites W2440156827 @default.
• W4321597578 cites W2605739746 @default.
• W4321597578 cites W2619199205 @default.
• W4321597578 cites W2770073247 @default.
• W4321597578 cites W2889246260 @default.
• W4321597578 cites W2911964244 @default.
• W4321597578 cites W2942851257 @default.
• W4321597578 cites W2971270198 @default.
• W4321597578 cites W2971859375 @default.
• W4321597578 cites W2977702035 @default.
• W4321597578 cites W3010047009 @default.
• W4321597578 cites W3016654606 @default.
• W4321597578 cites W3025306985 @default.
• W4321597578 cites W3025531840 @default.
• W4321597578 cites W3038144624 @default.
• W4321597578 cites W3038159662 @default.
• W4321597578 cites W3096796110 @default.
• W4321597578 cites W3096916113 @default.
• W4321597578 cites W3120410098 @default.
• W4321597578 cites W3138886979 @default.
• W4321597578 cites W3169921180 @default.
• W4321597578 cites W3212734031 @default.
• W4321597578 cites W4200112360 @default.
• W4321597578 cites W4205578146 @default.
• W4321597578 cites W4221054993 @default.
• W4321597578 cites W4245639438 @default.
• W4321597578 cites W4281907808 @default.
• W4321597578 cites W4296757692 @default.
• W4321597578 cites W4311488886 @default.
• W4321597578 doi "https://doi.org/10.3390/w15050856" @default.
• W4321597578 hasPublicationYear "2023" @default.
• W4321597578 type Work @default.
• W4321597578 citedByCount "1" @default.
• W4321597578 countsByYear W43215975782023 @default.
• W4321597578 crossrefType "journal-article" @default.
• W4321597578 hasAuthorship W4321597578A5036553945 @default.
• W4321597578 hasAuthorship W4321597578A5043096945 @default.
• W4321597578 hasAuthorship W4321597578A5058514086 @default.
• W4321597578 hasAuthorship W4321597578A5085365196 @default.
• W4321597578 hasBestOaLocation W43215975781 @default.
• W4321597578 hasConcept C105795698 @default.
• W4321597578 hasConcept C119857082 @default.
• W4321597578 hasConcept C133199616 @default.
• W4321597578 hasConcept C139945424 @default.
• W4321597578 hasConcept C169258074 @default.
• W4321597578 hasConcept C176783924 @default.
• W4321597578 hasConcept C18903297 @default.
• W4321597578 hasConcept C33923547 @default.
• W4321597578 hasConcept C39432304 @default.
• W4321597578 hasConcept C41008148 @default.
• W4321597578 hasConcept C44154836 @default.
• W4321597578 hasConcept C45804977 @default.
• W4321597578 hasConcept C46686674 @default.
• W4321597578 hasConcept C50644808 @default.
• W4321597578 hasConcept C5465570 @default.
• W4321597578 hasConcept C70153297 @default.
• W4321597578 hasConcept C8642999 @default.
• W4321597578 hasConcept C86803240 @default.
• W4321597578 hasConceptScore W4321597578C105795698 @default.
• W4321597578 hasConceptScore W4321597578C119857082 @default.
• W4321597578 hasConceptScore W4321597578C133199616 @default.
• W4321597578 hasConceptScore W4321597578C139945424 @default.
• W4321597578 hasConceptScore W4321597578C169258074 @default.
• W4321597578 hasConceptScore W4321597578C176783924 @default.
• W4321597578 hasConceptScore W4321597578C18903297 @default.
• W4321597578 hasConceptScore W4321597578C33923547 @default.
• W4321597578 hasConceptScore W4321597578C39432304 @default.

• next