FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4221097793> ?p ?o ?g. }

• W4221097793 endingPage "436" @default.
• W4221097793 startingPage "419" @default.
• W4221097793 abstract "Abstract. A long short-term memory (LSTM) neural network is proposed to predict hurricane-forced significant wave heights (SWHs) in the Caribbean Sea (CS) based on a dataset of 20 CS, Gulf of Mexico, and western Atlantic hurricane events collected from 10 buoys from 2010–2020. SWH nowcasting and forecasting are initiated using LSTM on 0, 3, 6, 9, and 12 h horizons. Through examining study cases Hurricanes Dorian (2019), Sandy (2012), and Igor (2010), results illustrate that the model is well suited to forecast hurricane-forced wave heights much more rapidly at a significantly cheaper computational cost compared to numerical wave models, with much less required expertise. Forecasts are highly accurate with regards to observations. For example, Hurricane Dorian nowcasts had correlation (R), root mean square error (RMSE), and mean absolute percentage error (MAPE) values of 0.99, 0.16 m, and 2.6 %, respectively. Similarly, on the 3, 6, 9, and 12 h forecasts, results produced R (RMSE; MAPE) values of 0.95 (0.51 m; 7.99 %), 0.92 (0.74 m; 10.83 %), 0.85 (1 m; 13.13 %), and 0.84 (1.24 m; 14.82 %), respectively. In general, the model can provide accurate predictions within 12 h (R≥0.8) and errors can be maintained at under 1 m within 6 h of forecast lead time. However, the model also consistently overpredicted the maximum observed SWHs. From a comparison of LSTM with a third-generation wave model, Simulating Waves Nearshore (SWAN), it was determined that when using Hurricane Dorian as a case example, nowcasts were far more accurate with regards to the observations. This demonstrates that LSTM can be used to supplement, but perhaps not replace, computationally expensive numerical wave models for forecasting extreme wave heights. As such, addressing the fundamental problem of phase shifting and other errors in LSTM or other data-driven forecasting should receive greater scrutiny from Small Island Developing States. To improve models results, additional research should be geared towards improving single-point LSTM neural network training datasets by considering hurricane track and identifying the hurricane quadrant in which buoy observations are made." @default.
• W4221097793 created "2022-04-03" @default.
• W4221097793 creator A5016763095 @default.
• W4221097793 creator A5061043121 @default.
• W4221097793 creator A5086966849 @default.
• W4221097793 creator A5090106151 @default.
• W4221097793 date "2022-03-28" @default.
• W4221097793 modified "2023-10-14" @default.
• W4221097793 title "Forecasting hurricane-forced significant wave heights using a long short-term memory network in the Caribbean Sea" @default.
• W4221097793 cites W2013535087 @default.
• W4221097793 cites W2028873346 @default.
• W4221097793 cites W2064675550 @default.
• W4221097793 cites W2283049126 @default.
• W4221097793 cites W2563295955 @default.
• W4221097793 cites W2564786372 @default.
• W4221097793 cites W2603537890 @default.
• W4221097793 cites W2738095052 @default.
• W4221097793 cites W2762459441 @default.
• W4221097793 cites W2774713021 @default.
• W4221097793 cites W2796003276 @default.
• W4221097793 cites W2800576522 @default.
• W4221097793 cites W2802223556 @default.
• W4221097793 cites W2886655107 @default.
• W4221097793 cites W2898147223 @default.
• W4221097793 cites W2899175022 @default.
• W4221097793 cites W2904995612 @default.
• W4221097793 cites W2905485534 @default.
• W4221097793 cites W2911950144 @default.
• W4221097793 cites W2914790694 @default.
• W4221097793 cites W2918462834 @default.
• W4221097793 cites W2929794125 @default.
• W4221097793 cites W2931330610 @default.
• W4221097793 cites W2944851425 @default.
• W4221097793 cites W2947665712 @default.
• W4221097793 cites W2951062783 @default.
• W4221097793 cites W2955373839 @default.
• W4221097793 cites W2961163591 @default.
• W4221097793 cites W2961841269 @default.
• W4221097793 cites W2971701951 @default.
• W4221097793 cites W2974988459 @default.
• W4221097793 cites W2979253597 @default.
• W4221097793 cites W2981339097 @default.
• W4221097793 cites W2989608117 @default.
• W4221097793 cites W2991900001 @default.
• W4221097793 cites W2996069565 @default.
• W4221097793 cites W3001719405 @default.
• W4221097793 cites W3005322115 @default.
• W4221097793 cites W3006889073 @default.
• W4221097793 cites W3008519024 @default.
• W4221097793 cites W3015420071 @default.
• W4221097793 cites W3017810119 @default.
• W4221097793 cites W3021007048 @default.
• W4221097793 cites W3025838273 @default.
• W4221097793 cites W3032834907 @default.
• W4221097793 cites W3037732878 @default.
• W4221097793 cites W3038525451 @default.
• W4221097793 cites W3042794743 @default.
• W4221097793 cites W3043629453 @default.
• W4221097793 cites W3044837070 @default.
• W4221097793 cites W3089561002 @default.
• W4221097793 cites W3093070619 @default.
• W4221097793 cites W3096600878 @default.
• W4221097793 cites W3096776150 @default.
• W4221097793 cites W3107212388 @default.
• W4221097793 cites W3109749236 @default.
• W4221097793 cites W3112439958 @default.
• W4221097793 cites W3116856324 @default.
• W4221097793 cites W3119602693 @default.
• W4221097793 cites W3127289629 @default.
• W4221097793 cites W3128160448 @default.
• W4221097793 cites W3130827992 @default.
• W4221097793 cites W3133294546 @default.
• W4221097793 cites W3133961016 @default.
• W4221097793 cites W3134447986 @default.
• W4221097793 cites W3134855123 @default.
• W4221097793 cites W3155272064 @default.
• W4221097793 cites W3159065393 @default.
• W4221097793 cites W3160507320 @default.
• W4221097793 cites W3160860783 @default.
• W4221097793 cites W3160970887 @default.
• W4221097793 cites W3161061077 @default.
• W4221097793 cites W3161608658 @default.
• W4221097793 cites W3162791930 @default.
• W4221097793 cites W3163993681 @default.
• W4221097793 cites W3166017306 @default.
• W4221097793 cites W3166332121 @default.
• W4221097793 cites W3168241862 @default.
• W4221097793 cites W3168992430 @default.
• W4221097793 cites W3169731451 @default.
• W4221097793 cites W3173994200 @default.
• W4221097793 cites W3175012453 @default.
• W4221097793 cites W3176925899 @default.
• W4221097793 cites W3179017016 @default.
• W4221097793 cites W3179444763 @default.
• W4221097793 cites W3194371715 @default.
• W4221097793 cites W3198716442 @default.
• W4221097793 cites W3201875070 @default.
• W4221097793 cites W3204510744 @default.

• next