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首页> 外文期刊>American journal of applied sciences >Estimation of Soils Electrical Resistivity using Artificial Neural Network Approach
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Estimation of Soils Electrical Resistivity using Artificial Neural Network Approach

机译：利用人工神经网络方法估计土壤电阻率

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摘要

The knowledge of the ground electrical resistivity is essential to ensure the protection of electrical and telecommunications networks. However, the monitoring of its values is an expensive task which takes long time. Therefore, its prediction is important. This study investigates on predicting soil electrical resistivity using Artificial Neural Networks. Nine sites of our city (Lome, TOGO) were considered. After characterization of the resistivity data collected on these sites, two models have been developed: Multilayer Perceptron (MLP) and Radial Basis Function (RBF) networks. Relative Root Mean Square Error (RRMSE) and R2 (Linear Correlation Coefficient) have been used to evaluate each model performance. For the MLP model, the configuration [ABCDEF] is the most efficient with the RRMSE = 12.00%, R~2 = 81.91% and 70 neurons under the hidden layer. For the RBF model, the configuration [BCDEF] is the most efficient with the RRMSE = 16.07%, R~2 = 69.97% and 100 neurons under the hidden layer. In general, the results exhibit that the MLP outcome configuration [ABCDEF] is the most efficient with the best RRMSE = 16.07% and R~2 = 69.97%. The letter A, B and C are the weather parameters and D, E, F are the geo-referenced coordinates of the measuring point. So far, research has not focused on predicting the electrical resistivity of the soil at a given location. Thus, the results of this study show that from meteorological data, it's possible to predict this electrical resistivity.
机译：对地电阻率的认识至关重要，以确保电信网络的保护。但是，监视其值是一个昂贵的任务，需要很长时间。因此，其预测很重要。本研究研究了使用人工神经网络预测土壤电阻率。考虑了我们城市的九个网站（洛美，多哥）。在这些站点上收集的电阻率数据进行了表征后，已经开发了两种模型：多层的Perceptron（MLP）和径向基函数（RBF）网络。相对根均方误差（RRMSE）和R2（线性相关系数）已被用于评估每个模型性能。对于MLP模型，配置[ABCDEF]是最有效的RRMSE = 12.00％，R〜2 = 81.91％和70个神经元在隐藏层下。对于RBF模型，配置[BCDEF]是最有效的RRMSE = 16.07％，R〜2 = 69.97％和100个神经元在隐藏层下。通常，结果表明，MLP结果构型[ABCDEF]是最有效的，最佳RRMSE = 16.07％，R〜2 = 69.97％。字母A，B和C是天气参数和D，E，F是测量点的地理参考坐标。到目前为止，研究没有专注于预测给定位置的土壤的电阻率。因此，该研究的结果表明，来自气象数据，可以预测这种电阻率。

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  • 来源
    《American journal of applied sciences》 |2019年第12期|43-58|共16页
  • 作者

    Kpomone Komla Apaloo-Bara; Adekunle Akim Salami; Mawugno Koffi Kodjo; Agbassou Guenoukpati; Sangue Oraleou Djandja; Koffi-Sa Bedja;

  • 作者单位

    Department of Electrical Engineering Ecole Nationale Superieure d 'Ingenieurs (ENSI) University of Lome Togo Laboratoire Genie-Electrique Ecole Nationale Superieure d 'Ingenieurs (ENSI) University of Lome Togo;

    Department of Electrical Engineering Ecole Nationale Superieure d 'Ingenieurs (ENSI) University of Lome Togo Laboratoire Genie-Electrique Ecole Nationale Superieure d 'Ingenieurs (ENSI) University of Lome Togo;

    Department of Electrical Engineering Ecole Nationale Superieure d 'Ingenieurs (ENSI) University of Lome Togo Laboratoire Genie-Electrique Ecole Nationale Superieure d 'Ingenieurs (ENSI) University of Lome Togo;

    Laboratoire Genie-Electrique Ecole Nationale Superieure d 'Ingenieurs (ENSI) University of Lome Togo;

    Laboratoire Genie-Electrique Ecole Nationale Superieure d 'Ingenieurs (ENSI) University of Lome Togo;

    Department of Electrical Engineering Ecole Nationale Superieure d 'Ingenieurs (ENSI) University of Lome Togo Laboratoire Genie-Electrique Ecole Nationale Superieure d 'Ingenieurs (ENSI) University of Lome Togo;

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  • 关键词

    Characterization; Prediction; Multilayer Perceptron; Radial Basis Function; Statistics;

    机译：特征;预言;多层的感觉;径向基函数;统计数据;

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