FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3199350497> ?p ?o ?g. }

• W3199350497 endingPage "57" @default.
• W3199350497 startingPage "57" @default.
• W3199350497 abstract "Precise soil moisture prediction is important for water management and logistics of on-farm operations. However, soil moisture is affected by various soil, crop, and meteorological factors, and it is difficult to establish ideal mathematical models for moisture prediction. We investigated various machine learning techniques for predicting soil moisture in the Red River Valley of the North (RRVN). Specifically, the evaluated machine learning techniques included classification and regression trees (CART), random forest regression (RFR), boosted regression trees (BRT), multiple linear regression (MLR), support vector regression (SVR), and artificial neural networks (ANN). The objective of this study was to determine the effectiveness of these machine learning techniques and evaluate the importance of predictor variables. The RFR and BRT algorithms performed the best, with mean absolute errors (MAE) of <0.040 m3 m−3 and root mean square errors (RMSE) of 0.045 and 0.048 m3 m−3, respectively. Similarly, RFR, SVR, and BRT showed high correlations (r2 of 0.72, 0.65 and 0.67 respectively) between predicted and measured soil moisture. The CART, RFR, and BRT models showed that soil moisture at nearby weather stations had the highest relative influence on moisture prediction, followed by 4-day cumulative rainfall and PET, subsequently followed by bulk density and Ksat." @default.
• W3199350497 created "2021-09-27" @default.
• W3199350497 creator A5024014159 @default.
• W3199350497 creator A5043633675 @default.
• W3199350497 creator A5076712098 @default.
• W3199350497 date "2021-09-23" @default.
• W3199350497 modified "2023-10-16" @default.
• W3199350497 title "Machine Learning for Predicting Field Soil Moisture Using Soil, Crop, and Nearby Weather Station Data in the Red River Valley of the North" @default.
• W3199350497 cites W1578138499 @default.
• W3199350497 cites W1578650557 @default.
• W3199350497 cites W1587637304 @default.
• W3199350497 cites W1607492013 @default.
• W3199350497 cites W1672537596 @default.
• W3199350497 cites W1678356000 @default.
• W3199350497 cites W1964785899 @default.
• W3199350497 cites W1969382373 @default.
• W3199350497 cites W1971984535 @default.
• W3199350497 cites W1974698067 @default.
• W3199350497 cites W1980965960 @default.
• W3199350497 cites W1987785621 @default.
• W3199350497 cites W1989700757 @default.
• W3199350497 cites W2004041476 @default.
• W3199350497 cites W2006947831 @default.
• W3199350497 cites W2009067114 @default.
• W3199350497 cites W2015305484 @default.
• W3199350497 cites W2020097894 @default.
• W3199350497 cites W2028003655 @default.
• W3199350497 cites W2028745710 @default.
• W3199350497 cites W2033392402 @default.
• W3199350497 cites W2035484453 @default.
• W3199350497 cites W2037931255 @default.
• W3199350497 cites W2054337296 @default.
• W3199350497 cites W2058731966 @default.
• W3199350497 cites W2059348875 @default.
• W3199350497 cites W2060192010 @default.
• W3199350497 cites W2070493638 @default.
• W3199350497 cites W2088794999 @default.
• W3199350497 cites W2090683582 @default.
• W3199350497 cites W2132995696 @default.
• W3199350497 cites W2135695572 @default.
• W3199350497 cites W2139525108 @default.
• W3199350497 cites W2144767777 @default.
• W3199350497 cites W2157963336 @default.
• W3199350497 cites W2162848293 @default.
• W3199350497 cites W2170128985 @default.
• W3199350497 cites W2177044256 @default.
• W3199350497 cites W2177299793 @default.
• W3199350497 cites W2188767531 @default.
• W3199350497 cites W2278830514 @default.
• W3199350497 cites W2307350467 @default.
• W3199350497 cites W2343168670 @default.
• W3199350497 cites W2496120601 @default.
• W3199350497 cites W2554960095 @default.
• W3199350497 cites W2588003345 @default.
• W3199350497 cites W2610495018 @default.
• W3199350497 cites W2613126452 @default.
• W3199350497 cites W2618718892 @default.
• W3199350497 cites W2786693279 @default.
• W3199350497 cites W2804415560 @default.
• W3199350497 cites W2899087191 @default.
• W3199350497 cites W2908031888 @default.
• W3199350497 cites W2911964244 @default.
• W3199350497 cites W2927513003 @default.
• W3199350497 cites W2944644291 @default.
• W3199350497 cites W2973218737 @default.
• W3199350497 cites W3019813984 @default.
• W3199350497 cites W3026483341 @default.
• W3199350497 cites W3035517615 @default.
• W3199350497 cites W3095322547 @default.
• W3199350497 cites W4230674625 @default.
• W3199350497 cites W4239510810 @default.
• W3199350497 cites W4300764748 @default.
• W3199350497 cites W94052953 @default.
• W3199350497 doi "https://doi.org/10.3390/soilsystems5040057" @default.
• W3199350497 hasPublicationYear "2021" @default.
• W3199350497 type Work @default.
• W3199350497 sameAs 3199350497 @default.
• W3199350497 citedByCount "13" @default.
• W3199350497 countsByYear W31993504972022 @default.
• W3199350497 countsByYear W31993504972023 @default.
• W3199350497 crossrefType "journal-article" @default.
• W3199350497 hasAuthorship W3199350497A5024014159 @default.
• W3199350497 hasAuthorship W3199350497A5043633675 @default.
• W3199350497 hasAuthorship W3199350497A5076712098 @default.
• W3199350497 hasBestOaLocation W31993504971 @default.
• W3199350497 hasConcept C105795698 @default.
• W3199350497 hasConcept C119857082 @default.
• W3199350497 hasConcept C12267149 @default.
• W3199350497 hasConcept C127413603 @default.
• W3199350497 hasConcept C139945424 @default.
• W3199350497 hasConcept C152877465 @default.
• W3199350497 hasConcept C153294291 @default.
• W3199350497 hasConcept C159390177 @default.
• W3199350497 hasConcept C169258074 @default.
• W3199350497 hasConcept C176864760 @default.
• W3199350497 hasConcept C187320778 @default.
• W3199350497 hasConcept C205649164 @default.
• W3199350497 hasConcept C24939127 @default.

• next