A flood inundation modelling using v-support vector machine regression model
A flood inundation modelling using v-support vector machine regression model
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DOI：
10.1016/j.engappai.2015.09.014
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
Yang Liu;G. Pender
中科院分区：
文献类型：
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作者：
Yang Liu;G. Pender
全二维（2D）流体动力学模型已被证明在广泛的应用领域是成功的。使用全二维模型的限制是它们昂贵的计算需求。洪水风险分析和模型不确定性分析通常需要进行数千次的数值模型运行和性能评估。然而，在实际应用程序中，根本没有足够的时间和资源来执行如此大量的模型运行。在本研究中，提出了一种计算框架，称为v-支持向量回归(SVR)-细网格模型（FGM）或线性回归(LR)-FGM，用于解决计算昂贵的模拟问题。v-SVR-FGM或LR-FGM的概念将通过使用非线性回归或线性回归模型进行数据预处理的少量精细网格模型（FGM）运行来演示。该近似模型用于预测FGM结果的形式，而不是运行耗时的FGM。该方法在不影响FGM精度的前提下，大大缩短了计算时间。仿真结果表明，该方法能够获得较好的预测结果（水深和流速），并节省了大量的计算机时间。
Full two dimensional (2D) hydrodynamic models have proven to be successful in a wide area of applications. The limitation of using full 2D models is their expensive computational requirement. The flood risk analysis and model uncertainty analysis usually need to run the numerical model and evaluate the performance thousands of times. However, in real world applications, there is simply not enough time and resources to perform such a huge number of model runs. In this study, a computational framework, known as v-Support Vector Regression (SVR)-Fine Grid Model (FGM) or linear regression (LR)-FGM, is presented for solving computationally expensive simulation problems. The concept of v-SVR-FGM or LR-FGM will be demonstrated via a small number of fine grid model (FGM) runs using a nonlinear regression or linear regression model with data preprocessing. The approximation model is performed in predicting the form of results of FGM instead of running the time consuming FGM. This approach can substantially reduce computational running time without loss of accuracy of FGM. The simulation results suggest that the proposed method is able to achieve good predictive results (water depth and velocity) as well as provide considerable savings in computer time.