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首页> 外文期刊>Journal of Hydroinformatics >Implementation of artificial neural network technique in the simulation of dam breach hydrograph
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Implementation of artificial neural network technique in the simulation of dam breach hydrograph

机译：人工神经网络技术在溃坝水位模拟中的实现。

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摘要

In the present study, two artificial neural networks were developed to simulate outflow hydrograph from earthen dam breach. The required data for the modelling were collected from literature, laboratory experiments and a physically based model (i.e. BREACH). For the laboratory modelling, five different materials were used for the construction of different dams of various sizes, and the process of the breach was recorded by two video cameras to record the breach growth as well as the output hydrograph. The genetic algorithm was also applied to divide the data into three statistically similar sub-sets for training, validation and test purposes. The obtained results demonstrate that the results of the artificial neural network (ANN) method are in good agreement with the observed values, and this method produces better results than existing classical methods. Also, the experiments show when cohesive strength is larger, the breach process becomes slower, and the peak outflow and the final width and depth of breach become smaller. Moreover, when the friction angle is larger, the breach process becomes slower, and the peak outflow and the final width and depth of breach become smaller. However, the rate of breach formation is particularly dependent upon the soil properties.
机译：在本研究中，开发了两个人工神经网络来模拟从土坝溃坝中流出的水文图。建模所需的数据是从文献，实验室实验和基于物理的模型（即BREACH）中收集的。对于实验室建模，使用了五种不同的材料来建造各种大小的不同水坝，并且通过两个摄像机记录了破坏的过程，以记录破坏的增长以及输出水位图。遗传算法还被用于将数据分为三个统计上相似的子集，以进行训练，验证和测试。所得结果表明，人工神经网络（ANN）方法的结果与观测值吻合良好，并且该方法比现有的经典方法具有更好的结果。而且，实验表明，当内聚强度较大时，断裂过程变慢，并且峰值流出以及断裂的最终宽度和深度变小。而且，当摩擦角较大时，破坏过程变慢，并且峰值流出和最终破坏的宽度和深度变小。但是，破坏形成的速率特别取决于土壤的性质。

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