FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3044173857> ?p ?o ?g. }

• W3044173857 endingPage "18" @default.
• W3044173857 startingPage "1" @default.
• W3044173857 abstract "Taiwan, being located on a path in the west Pacific Ocean where typhoons often strike, is often affected by typhoons. The accompanying strong winds and torrential rains make typhoons particularly damaging in Taiwan. Therefore, we aimed to establish an accurate wind speed prediction model for future typhoons, allowing for better preparation to mitigate a typhoon’s toll on life and property. For more accurate wind speed predictions during a typhoon episode, we used cutting-edge machine learning techniques to construct a wind speed prediction model. To ensure model accuracy, we used, as variable input, simulated values from the Weather Research and Forecasting model of the numerical weather prediction system in addition to adopting deeper neural networks that can deepen neural network structures in the construction of estimation models. Our deeper neural networks comprise multilayer perceptron (MLP), deep recurrent neural networks (DRNNs), and stacked long short-term memory (LSTM). These three model-structure types differ by their memory capacity: MLPs are model networks with no memory capacity, whereas DRNNs and stacked LSTM are model networks with memory capacity. A model structure with memory capacity can analyze time-series data and continue memorizing and learning along the time axis. The study area is northeastern Taiwan. Results showed that MLP, DRNN, and stacked LSTM prediction error rates increased with prediction time (1–6 hours). Comparing the three models revealed that model networks with memory capacity (DRNN and stacked LSTM) were more accurate than those without memory capacity. A further comparison of model networks with memory capacity revealed that stacked LSTM yielded slightly more accurate results than did DRNN. Additionally, we determined that in the construction of the wind speed prediction model, the use of numerically simulated values reduced the error rate approximately by 30%. These results indicate that the inclusion of numerically simulated values in wind speed prediction models enhanced their prediction accuracy." @default.
• W3044173857 created "2020-07-29" @default.
• W3044173857 creator A5039739337 @default.
• W3044173857 date "2020-07-23" @default.
• W3044173857 modified "2023-09-26" @default.
• W3044173857 title "Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions" @default.
• W3044173857 cites W1595881088 @default.
• W3044173857 cites W1705374184 @default.
• W3044173857 cites W1999508878 @default.
• W3044173857 cites W2033171150 @default.
• W3044173857 cites W2039544397 @default.
• W3044173857 cites W2042919630 @default.
• W3044173857 cites W2055412520 @default.
• W3044173857 cites W2057088198 @default.
• W3044173857 cites W2060376868 @default.
• W3044173857 cites W2064675550 @default.
• W3044173857 cites W2067714320 @default.
• W3044173857 cites W2070158856 @default.
• W3044173857 cites W2076675286 @default.
• W3044173857 cites W2082409570 @default.
• W3044173857 cites W2091828388 @default.
• W3044173857 cites W2094317913 @default.
• W3044173857 cites W2094427990 @default.
• W3044173857 cites W2110485445 @default.
• W3044173857 cites W2115354336 @default.
• W3044173857 cites W2123364787 @default.
• W3044173857 cites W2135789778 @default.
• W3044173857 cites W2136848157 @default.
• W3044173857 cites W2144668858 @default.
• W3044173857 cites W2191060491 @default.
• W3044173857 cites W2287279778 @default.
• W3044173857 cites W2518085669 @default.
• W3044173857 cites W2539982980 @default.
• W3044173857 cites W2591055632 @default.
• W3044173857 cites W2598232788 @default.
• W3044173857 cites W2616881109 @default.
• W3044173857 cites W2619995677 @default.
• W3044173857 cites W2620189642 @default.
• W3044173857 cites W2731442669 @default.
• W3044173857 cites W2756772778 @default.
• W3044173857 cites W2775425027 @default.
• W3044173857 cites W2781420345 @default.
• W3044173857 cites W2797869508 @default.
• W3044173857 cites W2802835792 @default.
• W3044173857 cites W2891094646 @default.
• W3044173857 cites W2895926968 @default.
• W3044173857 cites W2898721230 @default.
• W3044173857 cites W2901741023 @default.
• W3044173857 cites W2926988168 @default.
• W3044173857 cites W2936290605 @default.
• W3044173857 cites W2942992931 @default.
• W3044173857 cites W2946064216 @default.
• W3044173857 cites W2948374076 @default.
• W3044173857 cites W2949764339 @default.
• W3044173857 cites W2950895399 @default.
• W3044173857 cites W2956635826 @default.
• W3044173857 cites W2986473680 @default.
• W3044173857 cites W2994163750 @default.
• W3044173857 cites W2995703955 @default.
• W3044173857 cites W364427290 @default.
• W3044173857 cites W623374258 @default.
• W3044173857 doi "https://doi.org/10.1155/2020/5462040" @default.
• W3044173857 hasPublicationYear "2020" @default.
• W3044173857 type Work @default.
• W3044173857 sameAs 3044173857 @default.
• W3044173857 citedByCount "9" @default.
• W3044173857 countsByYear W30441738572020 @default.
• W3044173857 countsByYear W30441738572021 @default.
• W3044173857 countsByYear W30441738572023 @default.
• W3044173857 crossrefType "journal-article" @default.
• W3044173857 hasAuthorship W3044173857A5039739337 @default.
• W3044173857 hasBestOaLocation W30441738571 @default.
• W3044173857 hasConcept C108583219 @default.
• W3044173857 hasConcept C111919701 @default.
• W3044173857 hasConcept C121332964 @default.
• W3044173857 hasConcept C12186640 @default.
• W3044173857 hasConcept C133488467 @default.
• W3044173857 hasConcept C133875982 @default.
• W3044173857 hasConcept C145420912 @default.
• W3044173857 hasConcept C147168706 @default.
• W3044173857 hasConcept C153294291 @default.
• W3044173857 hasConcept C154945302 @default.
• W3044173857 hasConcept C161067210 @default.
• W3044173857 hasConcept C181654704 @default.
• W3044173857 hasConcept C205649164 @default.
• W3044173857 hasConcept C30038468 @default.
• W3044173857 hasConcept C33923547 @default.
• W3044173857 hasConcept C41008148 @default.
• W3044173857 hasConcept C50644808 @default.
• W3044173857 hasConcept C60908668 @default.
• W3044173857 hasConcept C61797465 @default.
• W3044173857 hasConcept C62520636 @default.
• W3044173857 hasConceptScore W3044173857C108583219 @default.
• W3044173857 hasConceptScore W3044173857C111919701 @default.
• W3044173857 hasConceptScore W3044173857C121332964 @default.
• W3044173857 hasConceptScore W3044173857C12186640 @default.
• W3044173857 hasConceptScore W3044173857C133488467 @default.
• W3044173857 hasConceptScore W3044173857C133875982 @default.

• next