Long short-term memory (LSTM)-based wind speed prediction during a typhoon for bridge traffic control
Tài liệu tham khảo
Abadi, 2016, Tensorflow: a system for large-scale machine learning
Al-Deen, 2006, A physical approach to wind speed prediction for wind energy forecasting, J. Wind Eng., 108, 349
Alexiadis, 1998, Short-term forecasting of wind speed and related electrical power, J. Sol Energy, 63, 61, 10.1016/S0038-092X(98)00032-2
Baker, 1992, Wind-induced accidents of road vehicles, J. Accid Anal Prev., 24, 559, 10.1016/0001-4575(92)90009-8
Barbounis, 2006, Locally recurrent neural networks for long-term wind speed and power prediction, J. Neurocomputing., 69, 466, 10.1016/j.neucom.2005.02.003
Blanchard, 2005
Blume, 2012, Supervised learning approaches to classify sudden stratospheric warming events, J. Atmos. Sci., 69, 1824, 10.1175/JAS-D-11-0194.1
Burges, 1998, A tutorial on support vector machines for pattern recognition, J. Data Min. Knowl. Discov., 2, 121, 10.1023/A:1009715923555
Burnham, 2002
Cai, 2018, Feature selection in machine learning: a new perspective, J. Neurocomputing., 300, 70, 10.1016/j.neucom.2017.11.077
Chen, 2007, Pattern recognition with SVM and dual-tree complex wavelets, J. Image Vis Comput., 25, 960, 10.1016/j.imavis.2006.07.009
Chen, 2018, Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization, J. Energy Convers. Manag., 165, 681, 10.1016/j.enconman.2018.03.098
Damousis, 2004, A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation, J. IEEE Trans. Energy Convers., 19, 352, 10.1109/TEC.2003.821865
De, 2018
DeMaria, 1987, Tropical cyclone track prediction with a barotropic spectral model, J. Mon. Weather Rev., 115, 2346, 10.1175/1520-0493(1987)115<2346:TCTPWA>2.0.CO;2
Deppermann, 1947, Notes on the origin and structure of Philippine typhoons, J. Bull Am Meteorol Soc., 28, 399, 10.1175/1520-0477-28.9.399
English
Fairclough, 2018, Theoretically optimal forms for very long-span bridges under gravity loading, Proc. Math. Phys. Eng. Sci., 474
Filik, 2016, Improved spatio-temporal linear models for very short-term wind speed forecasting, J. Energy, 9, 168
Focken, 2002, Short-term prediction of the aggregated power output of wind farms—a statistical analysis of the reduction of the prediction error by spatial smoothing effects, J. Wind Eng. Ind. Aerod., 90, 231, 10.1016/S0167-6105(01)00222-7
Fujii, 1992, On prediction of occurrence probability of severe wind by a typhoon, prediction of the sea-surface wind, J. Nat. Disaster Sci., 11, 125
Georgiou, 1983, Design wind speeds in regions dominated by tropical cyclones, J. Wind Eng. Ind. Aerod., 13, 139, 10.1016/0167-6105(83)90136-8
Guo, 2006, Safety analysis of moving road vehicles on a long bridge under crosswind, J. Eng. Mech., 132, 438, 10.1061/(ASCE)0733-9399(2006)132:4(438)
Hochreiter, 1997, Long short-term memory, J. Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735
Holland, 1980, An analytic model of the wind and pressure profiles in hurricanes, J. Mon. Weather Rev., 108, 1212, 10.1175/1520-0493(1980)108<1212:AAMOTW>2.0.CO;2
JTWC
Kadhem, 2017, Advanced wind speed prediction model based on a combination of weibull distribution and an artificial neural network, J. Energy, 10, 1744
Kariniotakis, 1996, Wind power forecasting using advanced neural networks models, J. IEEE Trans. Energy Convers., 11, 762, 10.1109/60.556376
Kim, 2019, Feasibility of a quasi-static approach in assessing side-wind hazards for running vehicles, J. Appl. Sci., 9, 3377
Kim, 2016, Vulnerability assessment for the hazards of crosswinds when vehicles cross a bridge deck, J. Wind Eng. Ind. Aerod., 156, 62, 10.1016/j.jweia.2016.07.005
Kim, 2020, How wind affects vehicles crossing a double-deck suspension bridge, J. Wind Eng. Ind. Aerod., 206, 104329, 10.1016/j.jweia.2020.104329
Kim, 2021, Decision framework for traffic control on sea-crossing bridges during strong winds, J. Bridge Eng., 10.1061/(ASCE)BE.1943-5592.0001741
Lei, 2009, A review on the forecasting of wind speed and generated power, Renew. J. Sustain. Energy Rev., 13, 915, 10.1016/j.rser.2008.02.002
Li, 2010, On comparing three artificial neural networks for wind speed forecasting, J. Appl. Energy, 87, 2313, 10.1016/j.apenergy.2009.12.013
Li, 2011, Bayesian adaptive combination of short-term wind speed forecasts from neural network models, J. Renew. Energy, 36, 352, 10.1016/j.renene.2010.06.049
Lin, 2005, The interaction of Supertyphoon Maemi (2003) with a warm ocean eddy, J. Mon. Weather Rev., 133, 2635, 10.1175/MWR3005.1
Maqsood, 2005, Application of soft computing models to hourly weather analysis in southern Saskatchewan, Canada, J. Eng. Appl. Artif. Intell., 18, 115, 10.1016/j.engappai.2004.08.019
Matsui, 1998, Extreme typhoon wind speeds considering differences in the average time between full-scale observations and typhoon model, J. JSCE, 506, 67
Meng, 1995, An analytical model for simulation of the wind field in a typhoon boundary layer, J. Wind Eng. Ind. Aerod., 56, 291, 10.1016/0167-6105(94)00014-5
More, 2003, Forecasting wind with neural networks, J. Mar. Struct., 16, 35, 10.1016/S0951-8339(02)00053-9
Russell, 1971, Probability distributions for hurricane effects, J. Waterw., 97
Sánchez, 2008, Adaptive combination of forecasts with application to wind energy, Int. J. Forecast., 24, 679, 10.1016/j.ijforecast.2008.08.008
Simiu, 1996
Tryggvason, 1976, Predicting wind-induced response in hurricane zones, J. Struct. Eng., 102, 2333
Üstün, 2006, Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel, J. Chemom. Intell. Lab. Syst., 81, 29, 10.1016/j.chemolab.2005.09.003
Wei, 2012, Wavelet support vector machines for forecasting precipitation in tropical cyclones: comparisons with GSVM, regression, and MM5, J. Weather and Forecasting, 27, 438, 10.1175/WAF-D-11-00004.1
Wei, 2014, Surface wind nowcasting in the penghu islands based on classified typhoon tracks and the effects of the central mountain range of taiwan, J. Weather and Forecasting, 29, 1425, 10.1175/WAF-D-14-00027.1
Wei, 2015, Forecasting surface wind speeds over offshore islands near Taiwan during tropical cyclones: comparisons of data-driven algorithms and parametric wind representations, J. Geophys. Res. Atmos., 120, 1826, 10.1002/2014JD022568
Wu, 2013, A benchmarking approach for comparing data splitting methods for modeling water resources parameters using artificial neural networks, J. Water Resour. Res., 49, 7598, 10.1002/2012WR012713
Zamani, 2008, Learning from data for wind–wave forecasting, J. Ocean Eng., 35, 953
Zhu, 2012, Wind tunnel investigations of aerodynamic coefficients of road vehicles on bridge deck, J. Fluid Struct., 30, 35, 10.1016/j.jfluidstructs.2011.09.002