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首页> 外文期刊>Environmental Processes >Modelling of Maximum Daily Water Temperature for Streams: Optimally Pruned Extreme Learning Machine (OPELM) versus Radial Basis Function Neural Networks (RBFNN)
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Modelling of Maximum Daily Water Temperature for Streams: Optimally Pruned Extreme Learning Machine (OPELM) versus Radial Basis Function Neural Networks (RBFNN)

机译：用于流的最大日水温度的建模：最佳修剪的极端学习机（OPELM）与径向基函数神经网络（RBFNN）

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摘要

This study proposed two data-driven models, namely the optimally pruned extreme learning machine (OPELM) and the radial basis functions neural networks (RBFNN) to predict maximum daily water temperature in streams. Air temperature (T_a,), flow discharge (Q) and the day of the year (DOY) were used as predictors. Four indicators, including the coefficient of correlation (R), the Willmott index of agreement (d), the root mean squared error (RMSE), and the mean absolute error (MAE) were used in evaluating the performances of different models. The present study was conducted according to four different scenarios. First, the OPELM and RBFNN models were developed and validated for each station separately. For the three other scenarios, the models were developed using data from one station and validated for the two other stations separately. Modelling results showed that in the proposed models T_a and Q may not be sufficiently informative and the addition of DOY significantly contributes to better capturing the seasonal pattern of the maximum daily water temperature in streams. Generally, OPELM models outperformed RBFNN models, and overall, the modelling results indicated that the OPELM models developed in this study can be effectively used for predicting maximum water temperature in streams.
机译：本研究提出了两个数据驱动的模型，即最佳修剪的极端学习机（opelm）和径向基函数神经网络（RBFNN），以预测流中的最大日水温度。空气温度（T_A，），流量放电（Q）和一年中的一天（DOY）被用作预测因子。四个指标，包括相关系数（R），威尔蒙特协议指数（D），根均方误差（RMSE）和平均绝对误差（MAE）用于评估不同模型的性能。根据四种不同的情景进行本研究。首先，为每个站单独开发并验证OpelM和RBFNN模型。对于其他三种情况，模型是使用来自一个站的数据开发的，并分别为两个其他站验证。建模结果表明，在所提出的模型中，T_A和Q可能不充分信息，并且添加DOY显着有助于更好地捕获流中最大日水温度的季节性模式。通常，Opelm型号优于RBFNN模型，总体而言，建模结果表明该研究中开发的欧宝模型可以有效地用于预测流中的最大水温。

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  • 来源
    《Environmental Processes》 |2019年第3期|789-804|共16页
  • 作者

    Senlin Zhu; Salim Heddam;

  • 作者单位

    State Key Laboratory of Hydrology-Water resources and Hydraulic Engineering Nanjing Hydraulic Research Institute Nanjing 210029 China;

    Faculty of Science Agronomy Department Hydraulics Division Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology University 20 Aout 1955 Route El Hadaik 26 Skikda BP Algeria;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Maximum water temperature; Air temperature; Day of the year; Flow discharge; OPELM; RBFNN;

    机译：最大水温;气温;一年中的一天;流量放电;opelm;rbfnn.;

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