Long short-term memory (LSTM)-based wind speed prediction during a typhoon for bridge traffic control

Jae-Yeong Lim1, Sejin Kim2, Ho-Kyung Kim3, Young-Kuk Kim4
1Department of Civil and Environmental Engineering, Seoul National University, Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
2Institute of Construction and Environmental Engineering, Seoul National University 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
3Department of Civil and Environmental Engineering, Institute of Construction and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, South Korea
4Bridge Management Section Chief, Busan Infrastructure Corporation, 203, Suyeonggangbyeon-daero Haeundae-gu, Busan, 48050, South Korea

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