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improving reliability of river flow forecasting using neural networks, wavelets and self-organising maps

机译：使用神经网络，小波和自组织图提高河流流量预报的可靠性

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摘要

Neural network (NN) models have gained much attention for river flow forecasting because of their ability to map complex non-linearities. However, the selection of appropriate length of training datasets is crucial and the uncertainty in predictions of the trained NNs with new datasets is a crucial problem. In this study, self-organising maps (SOM) are used to classify the datasets homogeneously and the performance of four types of NN models developed for daily discharge predictions - namely traditional NN, wavelet-based NN (WNN), bootstrap-based NN (BNN) and wavelet-bootstrap-based NN (WBNN) - is analysed for their applicability cluster-wise. SOM classified the training datasets into three clusters (i.e. cluster I, II and III) and the trained SOM is then used to assign testing datasets into these three clusters. Simulation studies show that the WBNN model performs better for the entire testing dataset as well as for values in clusters I and III; for cluster II the performance of BNN model is better compared with others for a 1-day lead time forecasting. Overall, it is found that the proposed methodology can enhance the accuracy and reliability of river flow forecasting.
机译：神经网络（NN）模型具有绘制复杂非线性的功能，因此在河流流量预测中引起了广泛关注。然而，选择合适的训练数据集长度至关重要，而使用新数据集预测训练后的神经网络的不确定性则是关键问题。在这项研究中，自组织图（SOM）用于对数据集进行均匀分类，并开发了用于日流量预测的四种类型的NN模型-传统NN，基于小波的NN（WNN），基于自举的NN（ BNN）和基于小波引导程序的NN（WBNN）-对它们的适用性进行了聚类分析。 SOM将训练数据集分为三个聚类（即聚类I，II和III），然后将训练后的SOM用于将测试数据集分配到这三个聚类中。仿真研究表明，WBNN模型对于整个测试数据集以及聚类I和III中的值都有较好的表现。对于群集II，对于1天的提前期预测，BNN模型的性能优于其他模型。总体而言，发现所提出的方法可以提高河流流量预报的准确性和可靠性。

著录项

  • 来源
    《Journal of Hydroinformatics》 |2013年第2期|486-502|共17页
  • 作者

    Mukesh K. Tiwari; Ki-Young Song; Chandranath Chatterjee; Madan M. Gupta;

  • 作者单位

    Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur, west Bengal 721 302, India;

    Intelligent Systems Research Laboratory, College of Engineering, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5A9, Canada;

    Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur, west Bengal 721 302, India;

    Intelligent Systems Research Laboratory, College of Engineering, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5A9, Canada;

  • 收录信息 美国《科学引文索引》(SCI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    bootstrap; cluster analysis; decomposition; forecasting; mahanadi river basin; river flow;

    机译：引导程序;聚类分析;分解;预测;马哈纳迪河流域;河流流量;

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