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A comparison between three short-term shoreline prediction models

机译：三种短期海岸线预测模型的比较

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摘要

Monitoring and management of shorelines along populated coastal areas is a very important task, but remains a difficult endeavor. The historical information used for short-term analysis and prediction are always underpinned by uncertainties associated with old data. Predictions of shoreline positions normally depend on the accuracy of the input data as well as the validity of the mathematical models used. With the requirement to study shoreline changes along the Parana (PR) coast in Brazil, it was necessary to obtain related cartographic information, which included temporal shoreline data obtained from orthophotos. In this contribution, photogrammetric together with GPS data are used to compare the capability of three shoreline prediction models; linear regression, robust parameter estimation, and neural network to predict the 2008 Parana shoreline position, which is then validated using the GPS measured position of 2008. The results indicate a MAPE (Mean Absolute Percentage Error) of 0.61% for the linear regression, 0.14% for the robust estimation, and 0.33% for the artificial neural network method. Although the coefficient of determinant (R~2) value for the neural network was the best, i.e., 0.997 compared to 0.994 for the robust model and 0.984 for the linear regression, its maximum deviation from the control values (i.e., 16.46) was almost twice that of robust model (7.63). On the one hand, the robust estimation model provides a more suitable approach for managing outliers in shoreline prediction, and also validating traditional methods such as linear regression. On the other hand, the neural network method offers an alternative approach to the robust prediction model. The results of the study highlightthe importance of a model choice for predicting the shoreline position.
机译：监测和管理沿海人口稠密地区的海岸线是一项非常重要的任务，但仍是一项艰巨的努力。用于短期分析和预测的历史信息始终以与旧数据相关的不确定性为基础。海岸线位置的预测通常取决于输入数据的准确性以及所用数学模型的有效性。由于需要研究巴西巴拉那（PR）沿海的海岸线变化，因此有必要获取相关的制图信息，其中包括从正射影像获得的临时海岸线数据。在这项贡献中，摄影测量技术与GPS数据一起用于比较三种海岸线预测模型的能力。线性回归，鲁棒参数估计和神经网络来预测2008年巴拉那海岸线的位置，然后使用GPS测量的2008年位置对其进行验证。结果表明，线性回归的MAPE（平均绝对百分比误差）为0.61％，0.14 ％用于鲁棒估计，而0.33％用于人工神经网络方法。尽管神经网络的行列式系数（R〜2）值最好，即0.997，而健壮模型的系数为0.994，线性回归的系数为0.984，但其与控制值的最大偏差（即16.46）几乎是是健壮模型（7.63）的两倍。一方面，鲁棒估计模型为管理海岸线预测中的异常值提供了更合适的方法，并且还验证了线性回归等传统方法。另一方面，神经网络方法为鲁棒预测模型提供了另一种方法。研究结果突出了模型选择对预测海岸线位置的重要性。

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  • 来源
    《Ocean & coastal management》 |2012年第12期|102-110|共9页
  • 作者

    Rodrigo Mikosz Goncalves; Joseph L. Awange; Claudia Pereira Krueger; Bernhard Heck; Leandro dos Santos Coelho;

  • 作者单位

    Department of Cartography Engineering, Federal University of Pemambuco (UFPE), Geodetic Science and Technology ofGeoinformation Post Graduation Program,Recife 50670-901. PE, Brazil;

    Department of Spatial Sciences, Curtin University, GPO Box U1987, Perth, WA 6845, Australia;

    Geodetic Science Post Graduation Program, Federal University of Parana (UFPR), Box 19.001, 81.531-990 Curitiba, PR, Brazil;

    Geodetic Institute, Karlsruhe Institute of Technology, Engler-Strasse 7, D-76131 Karlsruhe, Germany;

    Pontifical Catholic University of Parana, PUCPR Production and Systems Engineering Graduate Program, LAS/PPGEPS Imaculada Conceicao, 1155, 80215-901 Curitiba. PR, Brazil;

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