Application of copula method and neural networks for predicting peak outflow from breached embankments
Application of copula method and neural networks for predicting peak outflow from breached embankments
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DOI：
10.1016/j.jher.2013.11.004
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发表时间：
2014-08-01
影响因子：
2.8
通讯作者：
Golian, Saeed
中科院分区：
文献类型：
--
作者：
Hooshyaripor, Farhad;Tahershamsi, Ahmad;Golian, Saeed
可用数据数量有限是大多数水文和水力学研究中的一个常见问题，通常是溃坝分析。在大多数决策分析中，概率模型的构建是克服这种限制的关键步骤。为了分析溃口水库的洪峰流量，本文利用了两组数据，原始数据和合成数据。原始数据集收集了大量的历史溃坝和合成数据集的copula方法后，将有效变量（高度和溃坝后的水体积和峰值出流流量）之间的依赖结构。数据库分别用于训练两个人工神经网络（ANN）以及两个统计关系。分析结果表明，人工神经网络模型训练的合成数据集是最有竞争力的模型预测峰值流出的R-2为0.96和0.95的校准和测试步骤，分别。另一个人工神经网络模型也优于统计关系，R-2分别为0.94和0.87的校准和测试步骤。(C)2013年国际水文环境工程与研究协会亚太分部。Elsevier B. V.出版，保留所有权利。
The limited number of available data is a common problem in most hydrologic and hydraulic studies, typically dam breach analysis. Construction of a probabilistic model is a key step in most decision making analyses to overcome such limitation. To analyze peak outflow from breached embankments, this paper has utilized two sets of data, original and synthetic datasets. Original datasets were collected from numerous historical dam failures and synthetic datasets were generated by copula method after incorporating the dependence structure among effective variables (height and volume of water behind the dam at failure and peak outflow discharge). The databases were separately employed to train two artificial neural networks (ANNs) as well as two statistical relations. Analyzing the results showed that the ANN model trained with synthetic datasets was the most competitive model for predicting peak outflows having R-2 of 0.96 and 0.95 for calibration and testing steps, respectively. The other ANN model was also better than statistical relations with R-2 of 0.94 and 0.87 respectively for calibration and testing steps. (C) 2013 International Association for Hydro-environment Engineering and Research, Asia Pacific Division. Published by Elsevier B.V. All rights reserved.