Application of Kalman filter to short-term tide level prediction

被引:38
|
作者
Yen, PH
Jan, CD
Lee, YP
Lee, HF
机构
[1] Dept. of Hydr. and Oc. Engrg., Nat. Cheng Kung Univ., Tainan
关键词
D O I
10.1061/(ASCE)0733-950X(1996)122:5(226)
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Prediction of tide levels is one of the important problems in determining the schedule making of activities or constructions in coastal and oceanic area. Accurate predictions of tide levels could not be obtained without a long length (about one month or more) of tide measurements by conventional methods. A method of using a short length (a few days) of tide measurements to predict tide levels over a longer time is proposed in the present paper. In the method, a harmonic tide level model is used to predict tide levels. The parameters of the tide level model, that is, the amplitudes of the harmonic components, are estimated by Kalman filtering technique using a few-day tide record with the assumption of known angular frequencies. The proposed model is then applied to the short-term tide level prediction of the Kaohsiung Harbor, Taiwan. As a result, the tide levels predicted by the proposed method are in good agreement with the observed data.
引用
收藏
页码:226 / 231
页数:6
相关论文
共 50 条
  • [31] Kalman filter for short-term load forecasting: An hourly predictor of municipal load
    Falvo, M. C.
    Gastaldi, M.
    Nardecchia, A.
    Prudenzi, A.
    [J]. PROCEEDINGS OF THE 16TH IASTED INTERNATIONAL CONFERENCE ON APPLIED SIMULATION AND MODELLING, 2007, : 364 - +
  • [32] Noise-identified Kalman filter for short-term traffic flow forecasting
    Zhang, Shuangyi
    Song, Youyi
    Jiang, Dazhi
    Zhou, Teng
    Qin, Jing
    [J]. 2019 15TH INTERNATIONAL CONFERENCE ON MOBILE AD-HOC AND SENSOR NETWORKS (MSN 2019), 2019, : 462 - 466
  • [33] Application of a long short-term memory neural network algorithm fused with Kalman filter in UWB indoor positioning
    Yalin Tian
    Zengzeng Lian
    Penghui Wang
    Mengqi Wang
    Zhe Yue
    Huabin Chai
    [J]. Scientific Reports, 14
  • [34] Application of a long short-term memory neural network algorithm fused with Kalman filter in UWB indoor positioning
    Tian, Yalin
    Lian, Zengzeng
    Wang, Penghui
    Wang, Mengqi
    Yue, Zhe
    Chai, Huabin
    [J]. SCIENTIFIC REPORTS, 2024, 14 (01)
  • [35] Combining Profile Similarity and Kalman Filter for Real-World Applicable Short-Term Bus Travel Time Prediction
    Schwinger, Felix
    Frohnhofen, Clemens
    Wernz, Jonas
    Braun, Stefan
    Jarke, Matthias
    [J]. 2021 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC), 2021, : 3738 - 3745
  • [36] Application of LSTM in Short-term Traffic Flow Prediction
    Kang, Chuanli
    Zhang, Zhenyu
    [J]. 2020 IEEE 5TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION ENGINEERING (IEEE ICITE 2020), 2020, : 98 - 101
  • [37] Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter
    Sheng JIN
    Dian-hai WANG
    Cheng XU
    Dong-fang MA
    [J]. Journal of Zhejiang University-Science A(Applied Physics & Engineering), 2013, (04) : 231 - 243
  • [38] Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter
    Jin, Sheng
    Wang, Dian-hai
    Xu, Cheng
    Ma, Dong-fang
    [J]. JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE A, 2013, 14 (04): : 231 - 243
  • [39] Fuzzy State Transition and Kalman Filter Applied in Short-Term Traffic Flow Forecasting
    Ming-Jun, Deng
    Shi-Ru, Qu
    [J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2015, 2015