FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3100870722> ?p ?o ?g. }

• W3100870722 endingPage "213798" @default.
• W3100870722 startingPage "213783" @default.
• W3100870722 abstract "In recent years, with the large-scale reduction of Arctic sea ice, the supplement of chlorophyll sensor data in seawater has become an essential part of environmental assessment. Accurately predicting the chlorophyll sensor data in seawater is of great significance to protect the Arctic marine ecological environment. A machine learning prediction method combined with wavelet transform is proposed. This process uses data from upper ocean observation buoys placed in the Arctic Ocean (A.O.) to predict the sensor analogue of chlorophyll-a (C.A.) in the upper ocean of the Arctic Ocean. Choose the best wavelet transform method and prevent the LSTM gradient from disappearing. A model combining SAE (stacked autoencoder) Bi (bidirectional) LSTM (long short-term memory) and wavelet transform is proposed. Experiments were conducted to compare the predictive performance of buoy data input as univariate at two different times and locations in the Arctic Ocean. The results show that compared with other models (such as LSTM), in the SAE Bi LSTM model, the data of the two sites have the highest prediction accuracy. The best wavelet transform methods are fourth-order four-layer and first-order four-layer. The observational data of the Chukchi Sea from 2018 to 2019 obtained the best prediction results. The root mean square error (RMSE) value is 0.02003 volts; the average absolute error (MAE) is 0.0841 volts. This research provides a new method for predicting the chlorophyll sensor parameters in the upper ocean through the sea ice buoy observed at a given point, which helps to improve the accuracy of the ocean sensor parameter prediction on the Arctic ice buoy." @default.
• W3100870722 created "2020-11-23" @default.
• W3100870722 creator A5016870572 @default.
• W3100870722 creator A5017427566 @default.
• W3100870722 creator A5018952101 @default.
• W3100870722 creator A5035375708 @default.
• W3100870722 creator A5038421351 @default.
• W3100870722 creator A5059615599 @default.
• W3100870722 creator A5067235025 @default.
• W3100870722 creator A5069661444 @default.
• W3100870722 creator A5077729550 @default.
• W3100870722 creator A5082734009 @default.
• W3100870722 creator A5083061699 @default.
• W3100870722 date "2020-01-01" @default.
• W3100870722 modified "2023-09-30" @default.
• W3100870722 title "Computer Prediction of Seawater Sensor Parameters in the Central Arctic Region Based on Hybrid Machine Learning Algorithms" @default.
• W3100870722 cites W2002049881 @default.
• W3100870722 cites W2006457820 @default.
• W3100870722 cites W2008642613 @default.
• W3100870722 cites W2034139177 @default.
• W3100870722 cites W2062350618 @default.
• W3100870722 cites W2095250615 @default.
• W3100870722 cites W2124459709 @default.
• W3100870722 cites W2134591556 @default.
• W3100870722 cites W2412782625 @default.
• W3100870722 cites W2462302850 @default.
• W3100870722 cites W2493918211 @default.
• W3100870722 cites W2537163483 @default.
• W3100870722 cites W2553839055 @default.
• W3100870722 cites W2569837375 @default.
• W3100870722 cites W2769581371 @default.
• W3100870722 cites W2783859656 @default.
• W3100870722 cites W2789510130 @default.
• W3100870722 cites W2802586787 @default.
• W3100870722 cites W2810530902 @default.
• W3100870722 cites W2883557617 @default.
• W3100870722 cites W2917883546 @default.
• W3100870722 cites W2946309465 @default.
• W3100870722 cites W2950903499 @default.
• W3100870722 cites W2955427403 @default.
• W3100870722 cites W2958773239 @default.
• W3100870722 cites W2983267094 @default.
• W3100870722 cites W2983873244 @default.
• W3100870722 cites W2991014406 @default.
• W3100870722 cites W3020874323 @default.
• W3100870722 cites W3041928728 @default.
• W3100870722 cites W4255680435 @default.
• W3100870722 doi "https://doi.org/10.1109/access.2020.3038570" @default.
• W3100870722 hasPublicationYear "2020" @default.
• W3100870722 type Work @default.
• W3100870722 sameAs 3100870722 @default.
• W3100870722 citedByCount "3" @default.
• W3100870722 countsByYear W31008707222022 @default.
• W3100870722 countsByYear W31008707222023 @default.
• W3100870722 crossrefType "journal-article" @default.
• W3100870722 hasAuthorship W3100870722A5016870572 @default.
• W3100870722 hasAuthorship W3100870722A5017427566 @default.
• W3100870722 hasAuthorship W3100870722A5018952101 @default.
• W3100870722 hasAuthorship W3100870722A5035375708 @default.
• W3100870722 hasAuthorship W3100870722A5038421351 @default.
• W3100870722 hasAuthorship W3100870722A5059615599 @default.
• W3100870722 hasAuthorship W3100870722A5067235025 @default.
• W3100870722 hasAuthorship W3100870722A5069661444 @default.
• W3100870722 hasAuthorship W3100870722A5077729550 @default.
• W3100870722 hasAuthorship W3100870722A5082734009 @default.
• W3100870722 hasAuthorship W3100870722A5083061699 @default.
• W3100870722 hasBestOaLocation W31008707221 @default.
• W3100870722 hasConcept C105795698 @default.
• W3100870722 hasConcept C111368507 @default.
• W3100870722 hasConcept C11413529 @default.
• W3100870722 hasConcept C127313418 @default.
• W3100870722 hasConcept C136894858 @default.
• W3100870722 hasConcept C139945424 @default.
• W3100870722 hasConcept C154945302 @default.
• W3100870722 hasConcept C196216189 @default.
• W3100870722 hasConcept C197248824 @default.
• W3100870722 hasConcept C2776248883 @default.
• W3100870722 hasConcept C2779847632 @default.
• W3100870722 hasConcept C33923547 @default.
• W3100870722 hasConcept C39432304 @default.
• W3100870722 hasConcept C41008148 @default.
• W3100870722 hasConcept C47432892 @default.
• W3100870722 hasConcept C49204034 @default.
• W3100870722 hasConcept C518008717 @default.
• W3100870722 hasConcept C62649853 @default.
• W3100870722 hasConceptScore W3100870722C105795698 @default.
• W3100870722 hasConceptScore W3100870722C111368507 @default.
• W3100870722 hasConceptScore W3100870722C11413529 @default.
• W3100870722 hasConceptScore W3100870722C127313418 @default.
• W3100870722 hasConceptScore W3100870722C136894858 @default.
• W3100870722 hasConceptScore W3100870722C139945424 @default.
• W3100870722 hasConceptScore W3100870722C154945302 @default.
• W3100870722 hasConceptScore W3100870722C196216189 @default.
• W3100870722 hasConceptScore W3100870722C197248824 @default.
• W3100870722 hasConceptScore W3100870722C2776248883 @default.
• W3100870722 hasConceptScore W3100870722C2779847632 @default.
• W3100870722 hasConceptScore W3100870722C33923547 @default.
• W3100870722 hasConceptScore W3100870722C39432304 @default.

• next