FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4383877466> ?p ?o ?g. }

• W4383877466 endingPage "3475" @default.
• W4383877466 startingPage "3475" @default.
• W4383877466 abstract "Grassland gross primary productivity (GPP) is an important part of global terrestrial carbon flux, and its accurate simulation and future prediction play an important role in understanding the ecosystem carbon cycle. Machine learning has potential in large-scale GPP prediction, but its application accuracy and impact factors still need further research. This paper takes the Mongolian Plateau as the research area. Six machine learning methods (multilayer perception, random forest, Adaboost, gradient boosting decision tree, XGBoost, LightGBM) were trained using remote sensing data (MODIS GPP) and 14 impact factor data and carried out the prediction of grassland GPP. Then, using flux observation data (positions of flux stations) and remote sensing data (positions of non-flux stations) as reference data, detailed accuracy evaluation and comprehensive trade-offs are carried out on the results, and key factors affecting prediction performance are further explored. The results show that: (1) The prediction results of the six methods are highly consistent with the change tendency of the reference data, demonstrating the applicability of machine learning in GPP prediction. (2) LightGBM has the best overall performance, with small absolute error (mean absolute error less than 1.3), low degree of deviation (root mean square error less than 3.2), strong model reliability (relative percentage difference more than 5.9), and a high degree of fit with reference data (regression determination coefficient more than 0.97), and the prediction results are closest to the reference data (mean bias is only −0.034). (3) Enhanced vegetation index, normalized difference vegetation index, precipitation, land use/land cover, maximum air temperature, potential evapotranspiration, and evapotranspiration are significantly higher than other factors as determining factors, and the total contribution ratio to the prediction accuracy exceeds 95%. They are the main factors influencing GPP prediction. This study can provide a reference for the application of machine learning in GPP prediction and also support the research of large-scale GPP prediction." @default.
• W4383877466 created "2023-07-12" @default.
• W4383877466 creator A5006424102 @default.
• W4383877466 creator A5053525601 @default.
• W4383877466 creator A5077127911 @default.
• W4383877466 creator A5088548388 @default.
• W4383877466 creator A5090316208 @default.
• W4383877466 date "2023-07-10" @default.
• W4383877466 modified "2023-10-16" @default.
• W4383877466 title "Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland" @default.
• W4383877466 cites W2022727148 @default.
• W4383877466 cites W2024046085 @default.
• W4383877466 cites W2056845648 @default.
• W4383877466 cites W2070493638 @default.
• W4383877466 cites W2131372757 @default.
• W4383877466 cites W2151774045 @default.
• W4383877466 cites W2155632266 @default.
• W4383877466 cites W2158897782 @default.
• W4383877466 cites W2164270261 @default.
• W4383877466 cites W2167425576 @default.
• W4383877466 cites W2611941915 @default.
• W4383877466 cites W2744288739 @default.
• W4383877466 cites W2915329514 @default.
• W4383877466 cites W2948928491 @default.
• W4383877466 cites W2979377675 @default.
• W4383877466 cites W2998598378 @default.
• W4383877466 cites W3086051247 @default.
• W4383877466 cites W3096444413 @default.
• W4383877466 cites W3102476541 @default.
• W4383877466 cites W3104705096 @default.
• W4383877466 cites W3120480394 @default.
• W4383877466 cites W3120523171 @default.
• W4383877466 cites W3120535729 @default.
• W4383877466 cites W3124733045 @default.
• W4383877466 cites W3130489387 @default.
• W4383877466 cites W3134953537 @default.
• W4383877466 cites W3135215418 @default.
• W4383877466 cites W3138245278 @default.
• W4383877466 cites W3151532589 @default.
• W4383877466 cites W3171520305 @default.
• W4383877466 cites W3174075456 @default.
• W4383877466 cites W3203120451 @default.
• W4383877466 cites W3204115813 @default.
• W4383877466 cites W3205257834 @default.
• W4383877466 cites W3205859756 @default.
• W4383877466 cites W3210049843 @default.
• W4383877466 cites W3212169484 @default.
• W4383877466 cites W3213426324 @default.
• W4383877466 cites W3217152558 @default.
• W4383877466 cites W4200291934 @default.
• W4383877466 cites W4200343038 @default.
• W4383877466 cites W4200527363 @default.
• W4383877466 cites W4200607121 @default.
• W4383877466 cites W4211239584 @default.
• W4383877466 cites W4213321520 @default.
• W4383877466 cites W4214852079 @default.
• W4383877466 cites W4220931070 @default.
• W4383877466 cites W4221088685 @default.
• W4383877466 cites W4221093392 @default.
• W4383877466 cites W4281261379 @default.
• W4383877466 cites W4281699074 @default.
• W4383877466 cites W4283024644 @default.
• W4383877466 cites W4289336422 @default.
• W4383877466 cites W4293434834 @default.
• W4383877466 cites W4293661200 @default.
• W4383877466 cites W4294982806 @default.
• W4383877466 cites W4297325547 @default.
• W4383877466 cites W4297884545 @default.
• W4383877466 cites W4300960719 @default.
• W4383877466 cites W4309710878 @default.
• W4383877466 cites W4310179929 @default.
• W4383877466 cites W4310378889 @default.
• W4383877466 cites W4311367167 @default.
• W4383877466 cites W4313404035 @default.
• W4383877466 cites W4315631335 @default.
• W4383877466 cites W4317934282 @default.
• W4383877466 cites W4318043410 @default.
• W4383877466 cites W4319786232 @default.
• W4383877466 cites W4321168830 @default.
• W4383877466 cites W4322704105 @default.
• W4383877466 cites W4323046316 @default.
• W4383877466 cites W4324026671 @default.
• W4383877466 cites W4361204703 @default.
• W4383877466 cites W4364368096 @default.
• W4383877466 cites W4365516561 @default.
• W4383877466 cites W4367319515 @default.
• W4383877466 cites W4377247753 @default.
• W4383877466 cites W4377294766 @default.
• W4383877466 doi "https://doi.org/10.3390/rs15143475" @default.
• W4383877466 hasPublicationYear "2023" @default.
• W4383877466 type Work @default.
• W4383877466 citedByCount "1" @default.
• W4383877466 crossrefType "journal-article" @default.
• W4383877466 hasAuthorship W4383877466A5006424102 @default.
• W4383877466 hasAuthorship W4383877466A5053525601 @default.
• W4383877466 hasAuthorship W4383877466A5077127911 @default.
• W4383877466 hasAuthorship W4383877466A5088548388 @default.
• W4383877466 hasAuthorship W4383877466A5090316208 @default.

• next