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A hybrid wavelet de-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series

机译：水文气象时间序列的混合小波降噪与秩集对分析混合方法

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摘要

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. Compared to three other generic methods, the results generated by WD-REPA model presented invariably smaller error measures which means the forecasting capability of the WD-REPA model is better than other models. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series.
机译：目前，准确，快速地预报水文气象时间序列是缓解干旱和减轻洪灾的主要挑战。本文提出了一种混合方法，即小波降噪（WD）和秩集对分析（RSPA），该方法充分利用了两种方法的组合来改善水文气象时间序列的预测。 WD允许通过小波变换分解和重构时间序列，从而将噪声与原始序列分离。 RSPA是集合对分析的一种更可靠，更有效的版本，与WD集成在一起，形成了WD-RSPA混合方法。 WD-RSPA方法使用了两种类型的水文气象数据集，这些数据集具有不同的特征和在某些代表性站点上的人类影响程度不同。该方法还与其他三种通用方法进行了比较：常规自回归综合移动平均（ARIMA）方法，人工神经网络（ANN）（BP误差反向传播，MLP多层感知器和RBF径向基函数）以及RSPA单独。九个错误指标用于评估模型性能。与其他三种通用方法相比，WD-REPA模型产生的结果始终具有较小的误差，这意味着WD-REPA模型的预测能力要优于其他模型。结果表明，WD-RSPA是准确，可行和有效的。特别是，即使在时间序列中包括了极端事件，在本文比较的各种通用方法中，WD-RSPA也是最好的。

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  • 来源
    《Environmental research》 |2018年第1期|269-281|共13页
  • 作者

    Dong Wang; Alistair G. Borthwick; Handan He; Yuankun Wang; Jieyu Zhu; Yuan Lu; Pengcheng Xu; Xiankui Zeng; Jichun Wu; Lachun Wang; Xinqing Zou; Jiufu Liu; Ying Zou; Ruimin He;

  • 作者单位

    Key Laboratory of Surficial Geochemistry, MOE, Department of Hydrosciences, School of Earth Sciences and Engineering, Collaborative Innovation Center of South China Sea Studies, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, PR China;

    School of Engineering, The University of Edinburgh, Edinburgh EH9 3JL, UK,School of Engineering, The University of Edinburgh, St Edmund HalL Queen's Lane, Oxford OX1 4AR, UK;

    Key Laboratory of Surficial Geochemistry, MOE, Department of Hydrosciences, School of Earth Sciences and Engineering, Collaborative Innovation Center of South China Sea Studies, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, PR China;

    Key Laboratory of Surficial Geochemistry, MOE, Department of Hydrosciences, School of Earth Sciences and Engineering, Collaborative Innovation Center of South China Sea Studies, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, PR China;

    Key Laboratory of Surficial Geochemistry, MOE, Department of Hydrosciences, School of Earth Sciences and Engineering, Collaborative Innovation Center of South China Sea Studies, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, PR China;

    Key Laboratory of Surficial Geochemistry, MOE, Department of Hydrosciences, School of Earth Sciences and Engineering, Collaborative Innovation Center of South China Sea Studies, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, PR China;

    Key Laboratory of Surficial Geochemistry, MOE, Department of Hydrosciences, School of Earth Sciences and Engineering, Collaborative Innovation Center of South China Sea Studies, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, PR China;

    Key Laboratory of Surficial Geochemistry, MOE, Department of Hydrosciences, School of Earth Sciences and Engineering, Collaborative Innovation Center of South China Sea Studies, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, PR China;

    Key Laboratory of Surficial Geochemistry, MOE, Department of Hydrosciences, School of Earth Sciences and Engineering, Collaborative Innovation Center of South China Sea Studies, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, PR China;

    School of Geographic and Oceanographic science, Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing, PR China;

    School of Geographic and Oceanographic science, Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing, PR China;

    Nanjing Hydraulic Research Institute, Nanjing, PR China,State Key Laboratory of Hydrology, Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing, PR China;

    Nanjing Hydraulic Research Institute, Nanjing, PR China,State Key Laboratory of Hydrology, Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing, PR China;

    Nanjing Hydraulic Research Institute, Nanjing, PR China,State Key Laboratory of Hydrology, Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing, PR China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Data-driven model; Forecasting; Hydro-meteorological series; Rank-Set Pair Analysis; Wavelet de-noising;

    机译：数据驱动模型;预测;水文气象系列;秩集对分析;小波消噪;

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