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首页> 外文期刊>International Journal of Statistics and Probability >Modified Group Method of Data Handling for Flood Quantile Prediction at Ungauged Site
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Modified Group Method of Data Handling for Flood Quantile Prediction at Ungauged Site

机译：未凝固网站洪水分位式预测数据处理的修改群方法

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摘要

Handling flood quantile with little data is essential in managing water resources. In this paper, we propose a potential model called Modified Group Method of Data Handling (MGMDH) to predict the flood quantile at ungauged sites in Malaysia. In this proposed MGMDH model, the principal component analysis (PCA) method is matched to the group method of data handling (GMDH) with various transfer functions. The MGMDH model consists of four transfer functions: polynomial, sigmoid, radial basis function, and hyperbolic tangent sigmoid transfer functions. The prediction performance of MGMDH models is compared to the conventional GMDH model. The appropriateness and effectiveness of the proposed models are demonstrated with a simulation study. Cauchy distribution is used in the simulation study as a disturbance error. The implementation of Cauchy Distribution as an error disturbance in artificial data illustrates the performance of the proposed models if the extreme value or extreme event occurs in the data set. The simulation study may say that the MGMDH model is superior to other comparison models, namely LR, NLR, GMDH and ANN models. Another beauty of this proposed model is that it shows a strong prediction performance when multicollinearity is absent in the data set.
机译：处理洪水量少数据对于管理水资源至关重要。在本文中，我们提出了一种称为修改的数据处理方法（MGMDH）的潜在模型，以预测马来西亚未吞噬位点的洪水量。在该提议的MGMDH模型中，主成分分析（PCA）方法与具有各种传输功能的数据处理（GMDH）的组方法匹配。 MGMDH模型由四个转移功能组成：多项式，乙型，径向基函数和双曲线切线转移功能。将MGMDH模型的预测性能与传统的GMDH模型进行比较。拟议模型的适当性和有效性被仿真研究证明。 Cauchy分布用于模拟研究作为扰动误差。作为人工数据中的错误干扰的Cauchy分布的实现说明了如果在数据集中发生极值或极端事件，则提出模型的性能。仿真研究可以说MGMDH模型优于其他比较模型，即LR，NLR，GMDH和ANN模型。这种提出的模型的另一个美是在数据集中不存在多重性性时，它显示出强烈的预测性能。

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