YIN Z C, ZHOU B T, DUAN M K, et al. Climate extremes become increasingly fierce in China[J]. The Innovation, 2023, 4(2): 100406. doi: 10.1016/j.xinn.2023.100406

OU J P, DUAN Z D, CHANG L. Typhoon risk analysis for key coastal cities in southEast China[J]. Journal of Natural Disasters, 2002, 11(4): 9−17(in Chinese). doi: 10.3969/j.issn.1004-4574.2002.04.002

LI H H, OU J P. Spatiotemporal distribution characteristics of downburst in China[J]. Journal of Natural Disasters, 2015, 24(6): 9−18(in Chinese).

Editorial Department of China Journal of Highway and Transport. Review on China's bridge engineering research: 2024[J]. China Journal of Highway and Transport, 2024, 37(12): 1−160 (in Chinese). doi: 10.19721/j.cnki.1001-7372.2024.12.001

YANG M, GUO Y F, HUANG Y T. Wind power ultra-short-term prediction method based on NWP wind speed correction and double clustering division of transitional weather process[J]. Energy, 2023, 282: 128947. doi: 10.1016/j.energy.2023.128947

SOLBAKKEN K, BIRKELUND Y. Evaluation of the weather research and forecasting (WRF) model with respect to wind in complex terrain[J]. Journal of Physics: Conference Series, 2018, 1102: 012011. doi: 10.1088/1742-6596/1102/1/012011

KWUN J H, KIM Y K, SEO J W, et al. Sensitivity of MM5 and WRF mesoscale model predictions of surface winds in a typhoon to planetary boundary layer parameterizations[J]. Natural Hazards, 2009, 51(1): 63−77. doi: 10.1007/s11069-009-9402-3

HU J M, HENG J N, WEN J M, et al. Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm[J]. Renewable Energy, 2020, 162: 1208−1226. doi: 10.1016/j.renene.2020.08.077

YOUSUF M U, AL-BAHADLY I, AVCI E. Current perspective on the accuracy of deterministic wind speed and power forecasting[J]. IEEE Access, 2019, 7: 159547−159564. doi: 10.1109/access.2019.2951153

ORWIG K D, SCHROEDER J L. Near-surface wind characteristics of extreme thunderstorm outflows[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2007, 95(7): 565−584. doi: 10.1016/j.jweia.2006.12.002

TAO T Y, WANG H. Research progress and considerations in the research of non-stationary turbulent characteristics of typhoon wind fields[J]. Acta Aerodynamica Sinica, 2021, 39(4): 162−171(in Chinese). doi: 10.7638/kqdlxxb-2021.0063

JI J, LI H. State-of-the-arts in artificial intelligence for disaster prevention and mitigation of civil engineering[J]. Bulletin of National Natural Science Foundation of China, 2023, 37(5): 840−853(in Chinese).

XIN Y B, LIU Z W, SHAO X D. Buffeting response analysis of long-span cable-stayed bridge under the measured downburst[J]. Acta Aerodynamica Sinica, 2020, 38(4): 705−714(in Chinese). doi: 10.7638/kqdlxxb-2018.0199

FANG G S, ZHAO L, WEI M M, et al. Estimation and application of typhoon extreme wind loadings at typical long-span bridge sites in coastal regions[J]. Journal of Tongji University (Natural Science), 2021, 49(10): 1390−1398 (in Chinese). doi: 10.11908/j.issn.0253-374x.21002

HUANG Z, CHALABI Z S. Use of time-series analysis to model and forecast wind speed[J]. Journal of Wind Engineering and Industrial Aerodynamics, 1995, 56(2-3): 311−322. doi: 10.1016/0167-6105(94)00093-S

AKAIKE H. Fitting autoregressive models for prediction[J]. Annals of the Institute of Statistical Mathematics, 1969, 21(1): 243−247. doi: 10.1007/BF02532251

DURBIN J. Efficient estimation of parameters in moving-average models[J]. Biometrika, 1959, 46(3-4): 306−316. doi: 10.1093/biomet/46.3-4.306

SAID S E, DICKEY D A. Testing for unit roots in autoregressive-moving average models of unknown order[J]. Biometrika, 1984, 71(3): 599−607. doi: 10.1093/biomet/71.3.599

FISHWICK P A. Neural network models in simulation: A comparison with traditional modeling approaches[C]// 21st Conference on Winter Simulation - WSC '89, Washington, D. C. , USA. ACM, 1989. doi: 10.1145/76738.76828

LYDIA M, SURESH KUMAR S, IMMANUEL SELVAKUMAR A, et al. Linear and non-linear autoregressive models for short-term wind speed forecasting[J]. Energy Conversion and Management, 2016, 112: 115−124. doi: 10.1016/j.enconman.2016.01.007

BOX G E, JENKINS G M, REINSEL G C, et al. Time series analysis: Forcasting and control[M]. New Jersey: John Wiley Sons, Inc. , 2016.

WANG J J, LI L, NIU D X, et al. An annual load forecasting model based on support vector regression with differential evolution algorithm[J]. Applied Energy, 2012, 94: 65−70. doi: 10.1016/j.apenergy.2012.01.010

LIU H, MI X W, LI Y F. Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network[J]. Energy Conversion and Management, 2018, 156: 498−514. doi: 10.1016/j.enconman.2017.11.053

SANTAMARÍA-BONFIL G, REYES-BALLESTEROS A, GERSHENSON C. Wind speed forecasting for wind farms: A method based on support vector regression[J]. Renewable Energy, 2016, 85: 790−809. doi: 10.1016/j.renene.2015.07.004

NOOROLLAHI Y, ALI JOKAR M, KALHOR A. Using artificial neural networks for temporal and spatial wind speed forecasting in Iran[J]. Energy Conversion and Management, 2016, 115: 17−25. doi: 10.1016/j.enconman.2016.02.041

ISLAM M S, MOHANDES M, REHMAN S. Vertical extrapolation of wind speed using artificial neural network hybrid system[J]. Neural Computing and Applications, 2017, 28(8): 2351−2361. doi: 10.1007/s00521-016-2373-x

HO C Y, CHENG K S, ANG C H. Utilizing the random forest method for short-term wind speed forecasting in the coastal area of central Taiwan[J]. Energies, 2023, 16(3): 1374. doi: 10.3390/en16031374

WEI C C. Forecasting surface wind speeds over offshore islands near Taiwan during tropical cyclones: Comparisons of data-driven algorithms and parametric wind representations[J]. Journal of Geophysical Research: Atmospheres, 2015, 120(5): 1826−1847. doi: 10.1002/2014jd022568

ZHANG Z D, QIN H, LIU Y Q, et al. Long short-term memory network based on neighborhood gates for processing complex causality in wind speed prediction[J]. Energy Conversion and Management, 2019, 192: 37−51. doi: 10.1016/j.enconman.2019.04.006

MEMARZADEH G, KEYNIA F. A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets[J]. Energy Conversion and Management, 2020, 213: 112824. doi: 10.1016/j.enconman.2020.112824

JIANG W J, LIU B, LIANG Y, et al. Applicability analysis of transformer to wind speed forecasting by a novel deep learning framework with multiple atmospheric variables[J]. Applied Energy, 2024, 353: 122155. doi: 10.1016/j.apenergy.2023.122155

VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]. 3lst Conference on Neural Information Processing Systems, Long Beach, CA, USA, 2017.

WU H J, MENG K, FAN D, et al. Multistep short-term wind speed forecasting using transformer[J]. Energy, 2022, 261: 125231. doi: 10.1016/j.energy.2022.125231

CADENAS E, RIVERA W. Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model[J]. Renewable Energy, 2010, 35(12): 2732−2738. doi: 10.1016/j.renene.2010.04.022

LAWAL A, REHMAN S, ALHEMS L M, et al. Wind speed prediction using hybrid 1D CNN and BLSTM network[J]. IEEE Access, 2021, 9: 156672−156679. doi: 10.1109/access.2021.3129883

TASCIKARAOGLU A, UZUNOGLU M. A review of combined approaches for prediction of short-term wind speed and power[J]. Renewable and Sustainable Energy Reviews, 2014, 34: 243−254. doi: 10.1016/j.rser.2014.03.033

LI H M, WANG J Z, LU H Y, et al. Research and application of a combined model based on variable weight for short term wind speed forecasting[J]. Renewable Energy, 2018, 116: 669−684. doi: 10.1016/j.renene.2017.09.089

CHENG Z S, WANG J Y. A new combined model based on multi-objective salp swarm optimization for wind speed forecasting[J]. Applied Soft Computing, 2020, 92: 106294. doi: 10.1016/j.asoc.2020.106294

ZHAO N, SU Y, DAI X X, et al. A new decomposition-ensemble strategy fusion with correntropy optimization learning algorithms for short-term wind speed prediction[J]. Applied Energy, 2024, 369: 123589. doi: 10.1016/j.apenergy.2024.123589

YU C J, LI Y L, BAO Y L, et al. A novel framework for wind speed prediction based on recurrent neural networks and support vector machine[J]. Energy Conversion and Management, 2018, 178: 137−145. doi: 10.1016/j.enconman.2018.10.008

LIU D, NIU D X, WANG H, et al. Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm[J]. Renewable Energy, 2014, 62: 592−597. doi: 10.1016/j.renene.2013.08.011

SANTHOSH M, VENKAIAH C, VINOD KUMAR D M. Short-term wind speed forecasting approach using ensemble empirical mode decomposition and deep Boltzmann machine[J]. Sustainable Energy, Grids and Networks, 2019, 19: 100242. doi: 10.1016/j.segan.2019.100242

WEI D L, TIAN Z D. A hybrid multivariate multistep wind-speed forecasting model based on a deep-learning neural network[J]. Journal of Energy Engineering, 2024, 150(6): 04024035. doi: 10.1061/jleed9.eyeng-5474

LIU H, TIAN H Q, LIANG X F, et al. Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks[J]. Applied Energy, 2015, 157: 183−194. doi: 10.1016/j.apenergy.2015.08.014

ZHANG Y G, CHEN B, PAN G F, et al. A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting[J]. Energy Conversion and Management, 2019, 195: 180−197. doi: 10.1016/j.enconman.2019.05.005

WANG Y M, WU L. On practical challenges of decomposition-based hybrid forecasting algorithms for wind speed and solar irradiation[J]. Energy, 2016, 112: 208−220. doi: 10.1016/j.energy.2016.06.075

JIANG Y, HUANG G Q. Short-term wind speed prediction: Hybrid of ensemble empirical mode decomposition, feature selection and error correction[J]. Energy Conversion and Management, 2017, 144: 340−350. doi: 10.1016/j.enconman.2017.04.064

JIANG Y, HUANG G Q, PENG X Y, et al. A novel wind speed prediction method: Hybrid of correlation-aided DWT, LSSVM and GARCH[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2018, 174: 28−38. doi: 10.1016/j.jweia.2017.12.019

全国气象防灾减灾标准化技术委员会. GB/T 28591-2012, 风力等级[S]. 北京: 中国标准出版社, 2012.

ZHANG Z Y, LI S W, FENG H, et al. Machine learning for bridge wind engineering[J]. Advances in Wind Engineering, 2024, 1(1): 100002. doi: 10.1016/j.awe.2024.100002

CHEN Y R, WANG Y, DONG Z K, et al. 2-D regional short-term wind speed forecast based on CNN-LSTM deep learning model[J]. Energy Conversion and Management, 2021, 244: 114451. doi: 10.1016/j.enconman.2021.114451

LAIMA S J, LI W J, FENG H, et al. Research progress of machine learning in bridge wind engineering[J]. China Journal of Highway and Transport, 2023, 36(8): 1−13 (in Chinese). doi: 10.19721/j.cnki.1001-7372.2023.08.001

ZHANG C, WEI H K, ZHAO J S, et al. Short-term wind speed forecasting using empirical mode decomposition and feature selection[J]. Renewable Energy, 2016, 96: 727−737. doi: 10.1016/j.renene.2016.05.023

DING Y, YE X W, GUO Y. A multistep direct and indirect strategy for predicting wind direction based on the EMD-LSTM model[J]. Structural Control and Health Monitoring, 2023, 2023: 4950487. doi: 10.1155/2023/4950487

YU C J, LI Y L, ZHAO L Y, et al. A novel time-frequency recurrent network and its advanced version for short-term wind speed predictions[J]. Energy, 2023, 262: 125556. doi: 10.1016/j.energy.2022.125556

LIU H, CHEN C. Multi-objective data-ensemble wind speed forecasting model with stacked sparse autoencoder and adaptive decomposition-based error correction[J]. Applied Energy, 2019, 254: 113686. doi: 10.1016/j.apenergy.2019.113686

FENG H, QIAN W L, LAIMA S J. A machine learning-based probabilistic wind speed prediction model with multi-resolution data, quantile regression and bound estimation[J]. Engineering Structures, 2025, 322: 119098. doi: 10.1016/j.engstruct.2024.119098

SONG X D, YAN Y Y, QIN S Q, et al. Short-term wind speed prediction method for bridges based on adaptive kernel density estimation[J]. Bridge Construction, 2020, 50(5): 56−61(in Chinese). doi: 10.3969/j.issn.1003-4722.2020.05.009

JI J, SUN Y, KONG F D, et al. A construction approach to prediction intervals based on bootstrap and deep belief network[J]. IEEE Access, 2019, 7: 124185−124195. doi: 10.1109/access.2019.2938214

WANG H, ZHANG Y M, MAO J X, et al. A probabilistic approach for short-term prediction of wind gust speed using ensemble learning[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2020, 202: 104198. doi: 10.1016/j.jweia.2020.104198

XIE Y Y, LI C S, TANG G, et al. A novel deep interval prediction model with adaptive interval construction strategy and automatic hyperparameter tuning for wind speed forecasting[J]. Energy, 2021, 216: 119179. doi: 10.1016/j.energy.2020.119179

ZHU Q M, CHEN J F, SHI D Y, et al. Learning temporal and spatial correlations jointly: A unified framework for wind speed prediction[J]. IEEE Transactions on Sustainable Energy, 11(1): 509-523. DOI:10.1109/tste.2019.2897136

CHEN Y, ZHANG S, ZHANG W Y, et al. Multifactor spatio-temporal correlation model based on a combination of convolutional neural network and long short-term memory neural network for wind speed forecasting[J]. Energy Conversion and Management, 2019, 185: 783−799. doi: 10.1016/j.enconman.2019.02.018

WU B R, WANG L, ZENG Y R. Interpretable wind speed prediction with multivariate time series and temporal fusion transformers[J]. Energy, 2022, 252: 123990. doi: 10.1016/j.energy.2022.123990

WANGN H, KE S T, WANG T G. Wind loads characteristic of large wind turbine considering typhoon transit process[J]. Acta Aerodynamica Sinica, 2020, 38(5): 915−923 (in Chinese). doi: 10.7638/kqdlxxb-2019.0108

HE Y C, FU J Y, LI Q S, et al. Field measurement of the global structure and wind field of typhoons in South China[J]. Acta Aerodynamica Sinica, 2021, 39(4): 129−146(in Chinese). doi: 10.7638/kqdlxxb-2021.0078

ZHAO L, CUI W, FANG G S, et al. State-of-the-art review on typhoon wind environments and their effects on long-span bridges[J]. Advances in Wind Engineering, 2024, 1(1): 100007. doi: 10.1016/j.awe.2024.100007

国家质量监督检验检疫总局, 中国国家标准化管理委员会. GB/T 19201—2006, 热带气旋等级 [S]. 北京: 中国标准出版社, 2006.

BI K, XIE L, ZHANG H, et al. Accurate medium-range global weather forecasting with 3D neural networks[J]. Nature, 2023, 619(7970): 533−538. doi: 10.1038/s41586-023-06185-3

CHEN L, ZHONG X, LI H, et al. A machine learning model that outperforms conventional global subseasonal forecast models[J]. Nat Commun, 2024, 15(1): 6425. doi: 10.1038/s41467-024-50714-1

CHEN R, ZHANG W M, WANG X. Machine learning in tropical cyclone forecast modeling: A review[J]. Atmosphere, 2020, 11(7): 676. doi: 10.3390/atmos11070676

LI C X, LI Z. Multiple signal decomposition method for multi-step forecasting of typhoon wind speed[J]. Journal of Vibration, Measurement Diagnosis, 2019, 39(5): 1103-1110, 1139(in Chinese). doi: 10.16450/j.cnki.issn.1004-6801.2019.05.028

WEI C C, PENG P C, TSAI C H, et al. Regional forecasting of wind speeds during typhoon landfall in Taiwan: A case study of westward-moving typhoons[J]. Atmosphere, 2018, 9(4): 141. doi: 10.3390/atmos9040141

LIM J Y, KIM S, KIM H K, et al. Long short-term memory (LSTM)-based wind speed prediction during a typhoon for bridge traffic control[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2022, 220: 104788. doi: 10.1016/j.jweia.2021.104788

WEI X Y, XIANG Y, SHEN X D, et al. Multi-step forecasting model of wind speed considering influence of typhoon[J]. Automation of Electric Power Systems, 2021, 45(14): 30−37 (in Chinese). doi: 10.7500/AEPS20201005003

LI J L, SONG Z H, WANG X F, et al. A novel offshore wind farm typhoon wind speed prediction model based on PSO–Bi-LSTM improved by VMD[J]. Energy, 2022, 251: 123848. doi: 10.1016/j.energy.2022.123848

HUANG M F, LIU G X, WANG Y F, et al. Neural network forecasts of typhoon wind speeds coupled with WRF and measured data[J]. Journal of Building Structures, 2022, 43(3): 98−108 (in Chinese). doi: 10.14006/j.jzjgxb.2020.0563

HE J H, LIU Y, ZHU Y, et al. Physics-informed TPA-LSTM network applied to multi-step wind speed prediction of offshore wind farms under typhoon conditions[J]. Power System Technology, 2023, 47(10): 4152−4161(in Chinese).

SOLARI G, DE GAETANO P. Dynamic response of structures to thunderstorm outflows: Response spectrum technique vs time-domain analysis[J]. Engineering Structures, 2018, 176: 188−207. doi: 10.1016/j.engstruct.2018.08.062

MENG Z Y, YAN D C, ZHANG Y J. General features of squall lines in East China[J]. Monthly Weather Review, 2013, 141(5): 1629−1647. doi: 10.1175/mwr-d-12-00208.1

TAO T Y, JIN Z, WANG H, et al. Study on measured modal properties of a long-span cable-stayed bridge under squall line wind actions[J]. China Journal of Highway and Transport, 2023, 36(7): 138−146 (in Chinese). doi: 10.19721/j.cnki.1001-7372.2023.07.011

LI C X, CHI E N, HE L, et al. Prediction of nonstationary downburst wind velocity based on time-varying ARMA and EMD-PSO-LSSVM algorithms[J]. Journal of Vibration and Shock, 2016, 35(17): 33−38,51(in Chinese). doi: 10.13465/j.cnki.jvs.2016.17.006

ZHONG W, LI C X. Predicting of nonstationary downburst wind velocity based on extreme learning machines[J]. Journal of Shanghai University (Natural Science Edition), 2018, 24(3): 446−455(in Chinese). doi: 10.12066/j.issn.1007-2861.1838

ZHENG X F, ZHONG W, LI C X. Multivariate nonstationary fluctuating wind velocity prediction using kernel-based extreme learning machine[J]. Journal of Vibration and Shock, 2017, 36(18): 223−230(in Chinese). doi: 10.13465/j.cnki.jvs.2017.18.033

TAO T Y, SHI P, WANG H, et al. Short-term prediction of downburst winds: A double-step modification enhanced approach[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2021, 211: 104561. doi: 10.1016/j.jweia.2021.104561

PAN F, XIE F, XING Y L, et al. Wind-induced dynamic response of large steel tube frame for 1000 kV UHV transmission lines to thunderstorm downbursts[J]. Acta Aerodynamica Sinica, 2012, 30(4): 502−507(in Chinese). doi: 10.3969/j.issn.0258-1825.2012.04.012

EL DAMATTY A, ELAWADY A. Critical load cases for lattice transmission line structures subjected to downbursts: Economic implications for design of transmission lines[J]. Engineering Structures, 2018, 159: 213−226. doi: 10.1016/j.engstruct.2017.12.043

HUANG G Q, JIANG Y, PENG L L, et al. Characteristics of intense winds in mountain area based on field measurement: Focusing on thunderstorm winds[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2019, 190: 166−182. doi: 10.1016/j.jweia.2019.04.020

LI S P, LI X, YANG Q S, et al. Experimental investigation of the time-varying characteristics of the spanwise correlation of buffeting force on streamlined box girders subjected to thunderstorms[J]. China Journal of Highway and Transport, 2023, 36(5): 128−139 (in Chinese). doi: 10.19721/j.cnki.1001-7372.2023.05.011

HE S Y, WANG L, ZHU D Y, et al. Thunderstorm prediction method based on spatiotemporal memory decoupling RNN[J]. Systems Engineering and Electronics, 2023, 45(11): 3474−3480(in Chinese).

LIU Y Q, YANG L, CHEN M X, et al. A deep learning approach for forecasting thunderstorm gusts in the Beijing-Tianjin-Hebei Region[J]. Advances in Atmospheric Sciences, 2024, 41(7): 1342−1363. doi: 10.1007/s00376-023-3255-7

SHI P, WANG H, TAO T Y. Short-term wind speed forecasting of downburst based on improved VARX model[M]//Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations. Boca Raton: CRC Press, 2021: 1551-1555. doi: 10.1201/9780429279119-212