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首页> 外文期刊>Journal of Hydroinformatics >Conjunction of SOM-based feature extraction method and hybrid wavelet-ANN approach for rainfall-runoff modeling
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Conjunction of SOM-based feature extraction method and hybrid wavelet-ANN approach for rainfall-runoff modeling

机译：基于SOM的特征提取方法与混合小波-ANN方法联合进行降雨径流建模。

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In rainfall-runoff modeling, the wavelet-ANN model, which includes a wavelet transform to capture multi-scale features of the process, as well as an artificial neural network (ANN) to predict the runoff discharge, is a beneficial approach. One of the essential steps in any ANN-based development process is determination of dominant input variables. This paper presents a two-stage procedure to model the rainfall-runoff process of the Delaney Creek and Payne Creek Basins, Florida, USA. The two-stage procedure includes data pre-processing and model building stages. In the data preprocessing stage, a wavelet transform is used to decompose the rainfall and runoff time series into several sub-series at different scales. Subsequently, independent sub-series are chosen via a self-organizing map (SOM). In the model building stage, selected sub-series are imposed as input data to a feed-forward neural network (FFNN) to forecast runoff discharge. To make a better interpretation of the model efficiency, the proposed model is compared with the Auto Regressive Integrated Moving Average with exogenous input (ARIMAX) and with the ad hoc FFNN methods, without any data pre-processing. The results proved that the proposed model leads to better outcome especially in term of determination coefficient for detecting peak points (DCpeak).
机译：在降雨径流建模中，小波ANN模型是一种有益的方法，该模型包括一个小波变换以捕获过程的多尺度特征，以及一个人工神经网络（ANN）来预测径流流量。在任何基于ANN的开发过程中，基本步骤之一就是确定主要输入变量。本文提出了一个分两个阶段的程序来模拟美国佛罗里达州德莱尼溪和佩恩溪盆地的降雨径流过程。分为两个阶段的过程包括数据预处理和模型构建阶段。在数据预处理阶段，使用小波变换将降雨和径流时间序列分解为不同规模的几个子序列。随后，通过自组织图（SOM）选择独立的子系列。在模型构建阶段，将选定的子系列作为输入数据施加到前馈神经网络（FFNN），以预测径流量。为了更好地解释模型效率，将提出的模型与带有外部输入的自动回归综合移动平均值（ARIMAX）和临时FFNN方法进行了比较，而无需进行任何数据预处理。结果证明，所提出的模型具有更好的结果，特别是在用于检测峰点的确定系数（DCpeak）方面。

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