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首页> 外文期刊>Nordic hydrology >Modelling of runoff and sediment yield using ANN, LS-SVR, REPTree and M5 models
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Modelling of runoff and sediment yield using ANN, LS-SVR, REPTree and M5 models

机译：使用ANN，LS-SVR，REPTree和M5模型对径流和沉积物产量进行建模

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摘要

In this study, the performance evaluation of five machine learning models, namely, ANNLM, ANNSCG, least square-support vector regression (LS-SVR), reduced error pruning tree (REPTree) and M5, was carried out for predicting runoff and sediment in the Pokhariya watershed, India using hydro-meteorological variables as input. The input variables were selected using the trial-and-error procedure which represents the hydrological process in the watershed. The seven input variables to all the models comprised a combination of rainfall, average temperature, relative humidity, pan evaporation, sunshine duration, solar radiation and wind speed. The monthly runoff and sediment yield data were used to calibrate and validate all models for the years 2000 to 2008. Evaluation of models' performances were carried out using four statistical indices, i.e., Nash-Sutcliffe coefficient (NSE), coefficient of determination (R~2), percent bias (PBIAS) and RMSE-observations standard deviation ratio (RSR). Comparative analysis showed that the ANNLM model marginally outperformed the LS-SVR model and all the other models investigated during calibration and validation for runoff modelling whereas the LS-SVR model surpassed the artificial neural networks (ANN) model and other models for sediment yield modelling. Moreover, M5 model tree is better in simulating sediment yield and runoff than its near counterpart, the REPTree model, and marginally inferior when compared to LS-SVR and ANN models.
机译：在这项研究中，对五种机器学习模型（ANNLM，ANNSCG，最小二乘支持向量回归（LS-SVR），减少误差修剪树（REPTree）和M5）的性能进行了评估，以预测径流和泥沙印度波克里亚流域，使用水文气象变量作为输入。输入变量是使用反复试验的程序选择的，该程序代表流域中的水文过程。所有模型的七个输入变量包括降雨，平均温度，相对湿度，锅蒸发，日照时间，太阳辐射和风速的组合。月径流量和沉积物产量数据用于校准和验证2000年至2008年的所有模型。使用四个统计指标对模型的性能进行评估，即Nash-Sutcliffe系数（NSE），确定系数（R 〜2），百分比偏差（PBIAS）和RMSE观测标准偏差比（RSR）。对比分析表明，在径流模型的校准和验证过程中，ANNLM模型的性能略优于LS-SVR模型和所有其他模型，而LS-SVR模型则超过了人工神经网络（ANN）模型和其他用于沉积物产量模型的模型。此外，M5模型树在模拟沉积物产量和径流方面比其近端模型REPTree模型更好，并且与LS-SVR和ANN模型相比在边缘上较差。

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  • 来源
    《Nordic hydrology》 |2017年第6期|1489-1507|共19页
  • 作者

    Birendra Bharti; Ashish Pandey; S. K. Tripathi; Dheeraj Kumar;

  • 作者单位

    Department of Water Resources Development and Management, IIT Roorkee, Roorkee, Haridwar, 247667, India,corresponding author;

    Department of Water Resources Development and Management, IIT Roorkee, Roorkee, Haridwar, 247667, India;

    Department of Water Resources Development and Management, IIT Roorkee, Roorkee, Haridwar, 247667, India;

    Civil Engineering Department, IIT Roorkee, Roorkee, Haridwar, 247667, India;

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

    ANN; M5 model; machine learning technique; REPTree; runoff; sediment yield;

    机译：人工神经网络M5模型;机器学习技术;REPTree;径流;泥沙产量;

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