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首页> 外文期刊>Mathematical Problems in Engineering >Landslide Occurrence Prediction Using Trainable Cascade Forward Network and Multilayer Perceptron
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Landslide Occurrence Prediction Using Trainable Cascade Forward Network and Multilayer Perceptron

机译：基于可训练级联网络和多层感知器的滑坡发生预测

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摘要

Landslides are one of the dangerous natural phenomena that hinder the development in Penang Island, Malaysia. Therefore, finding the reliable method to predict the occurrence of landslides is still the research of interest. In this paper, two models of artificial neural network, namely, Multilayer Perceptron (MLP) and Cascade Forward Neural Network (CFNN), are introduced to predict the landslide hazard map of Penang Island. These two models were tested and compared using eleven machine learning algorithms, that is, Levenberg Marquardt, Broyden Fletcher Goldfarb, Resilient Back Propagation, Scaled Conjugate Gradient, Conjugate Gradient with Beale, Conjugate Gradient with Fletcher Reeves updates, Conjugate Gradient with Polakribiere updates, One Step Secant, Gradient Descent, Gradient Descent with Momentum and Adaptive Learning Rate, and Gradient Descent with Momentum algorithm. Often, the performance of the landslide prediction depends on the input factors beside the prediction method. In this research work, 14 input factors were used. The prediction accuracies of networks were verified using the Area under the Curve method for the Receiver Operating Characteristics. The results indicated that the best prediction accuracy of 82.89% was achieved using the CFNN network with the Levenberg Marquardt learning algorithm for the training data set and 81.62% for the testing data set.
机译：滑坡是阻止马来西亚槟城岛发展的危险自然现象之一。因此，寻找一种可靠的方法来预测滑坡的发生仍然是人们感兴趣的研究。本文介绍了两种人工神经网络模型，即多层感知器（MLP）和级联神经网络（CFNN），以预测槟城岛的滑坡灾害图。使用11种机器学习算法对这两个模型进行了测试和比较，分别是Levenberg Marquardt，Broyden Fletcher Goldfarb，弹性后向传播，缩放共轭梯度，Beale的共轭梯度，Fletcher Reeves更新的共轭梯度，Polakribiere更新的共轭梯度，一个步进割线，梯度下降，具有动量和自适应学习率的梯度下降以及具有动量算法的梯度下降。通常，滑坡预测的性能取决于预测方法之外的输入因素。在这项研究工作中，使用了14个输入因子。使用曲线下面积作为接收器工作特性，验证了网络的预测精度。结果表明，使用CFNN网络和Levenberg Marquardt学习算法的训练数据集可达到82.89％的最佳预测精度，而测试数据集则可达到81.62％。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第18期|512158.1-512158.9|共9页
  • 作者

    Al-Batah Mohammad Subhi; Alkhasawneh Mutasem Sh.; Tay Lea Tien; Ngah Umi Kalthum; Lateh Habibah Hj; Isa Nor Ashidi Mat;

  • 作者单位

    Jadara Univ, Fac Sci & Informat Technol, Dept Software Engn, Irbid 2001, Jordan;

    Univ Sains Malaysia, Sch Elect & Elect Engn, Nibong Tebal 14300, Penang, Malaysia;

    Univ Sains Malaysia, Sch Elect & Elect Engn, Nibong Tebal 14300, Penang, Malaysia;

    Univ Sains Malaysia, Sch Elect & Elect Engn, Nibong Tebal 14300, Penang, Malaysia;

    Univ Sains Malaysia, Sch Distance Educ, George Town 11600, Malaysia;

    Univ Sains Malaysia, Sch Elect & Elect Engn, Nibong Tebal 14300, Penang, Malaysia;

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