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首页> 外文期刊>Journal of Hydroinformatics >Decision support system for predicting tsunami characteristics along coastline areas based on database modelling development
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Decision support system for predicting tsunami characteristics along coastline areas based on database modelling development

机译：基于数据库建模开发的沿海地区海啸特征预测支持系统

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摘要

Tsunamis are extraordinary occurrences that are difficult to identify; most of the incidents have no recorded predictions and tsunamis are generally infrequent events with poor data acquisition. The development of a tsunami database system has become important for improving the management of information with regard to a tsunami early warning system for vulnerable communities along coastline areas. Numerical modelling is usually employed to simulate the wave height and travel time of a wave arriving at a coastline area. However, numerical modelling for tsunami prediction is too time-consuming to be useful as an early warning system for the mitigation of tsunami-related damage and loss. Therefore, this model was used to develop a tsunami database system, based on hypothetical data, in order to develop a recognition pattern for a neural network learning process that will improve the speed and accuracy of tsunami prediction. To improve the accuracy of numerical modelling and observation, an adjustment was established for an advanced training process which used a generalised regression neural network. In other words, the training and testing datasets were obtained by correcting near-field tsunami numerical models from hypothetical earthquakes. The case study was performed on part of the Southern Pangandaran coastline in West Java, Indonesia.
机译：海啸是难以识别的异常事件。大多数事件没有记录的预测，海啸通常是不经常发生的事件，数据获取不佳。海啸数据库系统的开发对于改善沿海地区脆弱社区海啸预警系统的信息管理已变得至关重要。通常采用数值模型来模拟到达海岸线地区的波的波高和传播时间。但是，用于海啸预测的数值模型太耗时，无法用作减轻海啸相关损害和损失的预警系统。因此，该模型用于基于假设数据开发海啸数据库系统，以便为神经网络学习过程开发识别模式，从而提高海啸预测的速度和准确性。为了提高数值建模和观测的准确性，对使用通用回归神经网络的高级培训过程进行了调整。换句话说，通过校正假设地震的近场海啸数值模型获得训练和测试数据集。该案例研究在印度尼西亚西爪哇省的南部Pangandaran海岸线的一部分上进行。

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  • 来源
    《Journal of Hydroinformatics》 |2011年第1期|p.96-109|共14页
  • 作者

    Iwan K. Hadihardaja; Hamzah Latief; lyan E. Mulia;

  • 作者单位

    Water Resources Engineering Research Division, Faculty of Civil and Environmental Engineering, Institut Teknologi Bandung, Indonesia, JI. Ganesha No.10, Bandung 40132, West Java, Indonesia;

    Oceanography Research Division, Faculty of Sciences and Mineral Technology, Institut Teknologi Bandung, Indonesia, JI. Ganesha No.10, Bandung 40132, West Java, Indonesia;

    Water Resources Engineering Research Division, Faculty of Civil and Environmental Engineering, Institut Teknologi Bandung, Indonesia, JI. Ganesha No.10, Bandung 40132, West Java, Indonesia;

  • 收录信息 美国《科学引文索引》(SCI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    correction factor; generalised regression neural network; hard and soft computing;

    机译：校正因子;广义回归神经网络硬和软计算;

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