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Application of Artificial Neural Network (ANN) to improve forecasting of sea level

机译：人工神经网络（ANN）在改善海平面预报中的应用

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摘要

To improve on forecasting of tidal water level beyond harmonic analysis requires the incorporation of meteorological variables in the analysis. This suggests the use of Artificial Neural Networks (ANN) as an optimum tool to train and translate the combined influence of meteorological and astronomical forcing to predict sea level variations and reduce the margin of error of close to 50% (from 26% to 12%). To accomplish this, the ANN was trained by using hourly time series of atmospheric pressure, wind, and harmonically derived tides for 1982 as input data and hourly time series of measured tides as output data. The meteorological data were obtained from Sao Sebasti3o (SP) and Ponta da Armacao (RJ), and the sea level data from Cananeia (SP) and Ilha Fiscal (RJ). Data gaps in the time series were interpolated based on FFT analysis. To forecast water levels, the 1983 meteorological time series was used as input data, and compared the resulting water level outputs to the water level measurements for the same period. The ANN served as a very good forecasting tool for sea level variability. In the case of Cananeia, with several meteorological data gaps, the comparison was less successful as compared to the Ilha Fiscal results, besides this, there is a local influence of the estuary flows, a variable not considered that could answers for the remaining 12% of the correlation. The coefficient of correlation between predicted and measured water level time series at Cananeia was 0.88 and at Ilha Fiscal 0.98. This kind of improvement can be used for port terminals and marinas, for handling incoming and outgoing ships and boats more safely through the navigation channels in the estuaries. It is applicable and useful information for decision makers in management activities in the coastal area.
机译：除谐波分析外，要改善对潮汐水位的预报，还需要在分析中纳入气象变量。这表明使用人工神经网络（ANN）作为最佳工具来训练和转换气象和天文强迫的综合影响，以预测海平面变化并将误差率降低近50％（从26％降至12％） ）。为此，通过使用1982年的大气压，风和谐波导出潮汐的小时时间序列作为输入数据，并以实测潮汐的小时时间序列作为输出数据来训练ANN。气象数据来自圣塞巴斯蒂3o（SP）和Ponta da Armacao（RJ），海平面数据来自Cananeia（SP）和Ilha Fiscal（RJ）。根据FFT分析对时间序列中的数据间隙进行插值。为了预测水位，将1983年气象时间序列用作输入数据，并将所得的水位输出与同期的水位测量值进行比较。人工神经网络是海平面变化的很好的预测工具。在Cananeia的情况下，由于存在多个气象数据缺口，与Ilha财政结果相比，该比较的成功率较低，此外，河口流量受到局部影响，该变量未被认为可以解决其余12％的问题。相关性Cananeia的预计水位时间序列与实测水位时间序列之间的相关系数是0.88，而Ilha Fiscal为0.98。这种改进可用于港口码头和码头，以便通过河口的导航通道更安全地处理进出港的船只。对于决策者在沿海地区的管理活动，它是适用且有用的信息。

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  • 来源
    《Ocean & coastal management》 |2012年第1期|p.101-110|共10页
  • 作者

    Alessandro Filippo; Audalio Rebelo Torres; Bjorn Kjerfve; Andre Monat;

  • 作者单位

    Departamento de Oceanografia Fisica, Faculdade de Oceanografia, Universidade do Estado do Rio de Janeiro - UERJ, Rua Sdo Francisco Xavier, 524, 4o andar. bloco E, sola 4017, Rio de Janeiro, RJ, Brazil;

    Departamento de Meceorologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil;

    World Maritime University, S-201 24 Malmd, Sweden, Departments of Geography/Oceanography, Texas A&M University, College Station, TX, USA;

    Programa de Pos Graduacao ESDI, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, RJ, Brazil;

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