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首页> 外文期刊>Water Resources Management >Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)
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Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)

机译：大坝水位的每日预测：将支持向量机（SVM）模型与自适应神经模糊推理系统（ANFIS）进行比较

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摘要

Reservoir planning and management are critical to the development of the hydrological field and necessary to Integrated Water Resources Management. The growth of forecasting models has resulted in an excellent model known as the Support Vector Machine (SVM). This model uses linearly separable patterns based on an optimal hyperplane, which are extended to non-linearly separable patterns by transforming the raw data to map into a new space. SVM can find a global optimal solution equipped with Kernel functions. These Kernel functions have high flexibility in the forecasting computation, enabling data to be mapped at a higher and infinite-dimensional space in an implicit manner. This paper presents a new solution to the expert system, using SVM to, forecast the daily dam water level of the Klang gate. Four categories are identified to determine the best model: the input scenario, the type of SVM regression, the number of V-fold cross-validation and the time lag. The best input scenario employs both the rainfall R(t-i) and the dam water level L(t-i). Type 2 SVM regression is selected as the best regression type, and 5-fold cross-validation produces the most accurate results. The results are compared with those obtained using ANFIS: all the RMSE, MAE and MAPE values prove that SVM is a superior model to ANFIS. Finally, all the results are combined to determine the best time lag, resulting in R(t-2) L(t-2) for the best model with only 1.64 % error.
机译：水库的规划和管理对水文领域的发展至关重要，对于水资源综合管理至关重要。预测模型的发展产生了一个出色的模型，称为支持向量机（SVM）。该模型使用基于最佳超平面的线性可分离模式，通过将原始数据转换为映射到新空间，这些模式可扩展为非线性可分离模式。 SVM可以找到配备内核功能的全局最佳解决方案。这些内核功能在预测计算中具有很高的灵活性，从而可以以隐式方式将数据映射到更高的无限维空间。本文提出了一种新的专家系统解决方案，利用支持向量机来预测巴生闸的每日大坝水位。确定了四个类别以确定最佳模型：输入方案，SVM回归的类型，V折交叉验证的数量和时间滞后。最佳输入方案同时使用降雨R（t-i）和大坝水位L（t-i）。选择2型SVM回归作为最佳回归类型，并且5倍交叉验证可产生最准确的结果。将结果与使用ANFIS获得的结果进行比较：所有RMSE，MAE和MAPE值都证明SVM是优于ANFIS的模型。最后，将所有结果组合起来以确定最佳时间滞后，得出最佳模型的R（t-2）L（t-2）仅具有1.64％的误差。

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  • 来源
    《Water Resources Management》 |2013年第10期|3803-3823|共21页
  • 作者

    Afiq Hipni; Ahmed El-shafie; Ali Najah; Othman Abdul Karim; Aini Hussain; Muhammad Mukhlisin;

  • 作者单位

    Department of Civil and Structural Engineering, Faculty of Engineering & Built Environment, University Kebangsaan Malaysia, Bangi, Selangor, Malaysia;

    Department of Civil and Structural Engineering, Faculty of Engineering & Built Environment, University Kebangsaan Malaysia, Bangi, Selangor, Malaysia;

    Department of Civil and Structural Engineering, Faculty of Engineering & Built Environment, University Kebangsaan Malaysia, Bangi, Selangor, Malaysia;

    Department of Civil and Structural Engineering, Faculty of Engineering & Built Environment, University Kebangsaan Malaysia, Bangi, Selangor, Malaysia;

    Department of Civil and Structural Engineering, Faculty of Engineering & Built Environment, University Kebangsaan Malaysia, Bangi, Selangor, Malaysia;

    Department of Civil and Structural Engineering, Faculty of Engineering & Built Environment, University Kebangsaan Malaysia, Bangi, Selangor, Malaysia,Department of Civil Engineering, Polytechnic Negeri Semarang, Semarang, Indonesia;

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

    Support vector machine; Dam water levels; Klang gate;

    机译：支持向量机;大坝水位;巴生门;

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