• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>American journal of applied sciences >Estimation of Soils Electrical Resistivity using ArtificialNeural Network Approach
【24h】

Estimation of Soils Electrical Resistivity using ArtificialNeural Network Approach

机译：人工神经网络法估算土壤电阻率

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

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%, R2 = 81.91% and 70 neurons under the hidden layer. For the RBF model, the configuration [BCDEF] is the most efficient with theRRMSE = 16.07%, R2 = 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 R2 = 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：地面电阻率的知识对于确保电气和电信网络的保护至关重要。但是，对其值的监视是一项昂贵的任务，需要很长时间。因此，其预测很重要。本研究调查了使用人工神经网络预测土壤电阻率的方法。考虑了我们城市的9个地点（洛美，多哥）。在表征了在这些站点上收集的电阻率数据后，已开发出两个模型：多层感知器（MLP）和径向基函数（RBF）网络。相对均方根误差（RRMSE）和R2（线性相关系数）已用于评估每个模型的性能。对于MLP模型，配置[ABCDEF]最为有效，RRMSE = 12.00％，R2 = 81.91％，并且在隐藏层下有70个神经元。对于RBF模型，配置[BCDEF]效率最高，RRMSE = 16.07％，R2 = 69.97％，并且在隐藏层下有100个神经元。通常，结果表明MLP结果配置[ABCDEF]是最有效的，RRMSE = 16.07％，R2 = 69.97％。字母A，B和C是天气参数，字母D，E，F是测量点的地理参考坐标。到目前为止，研究还没有集中在预测给定位置的土壤电阻率。因此，这项研究的结果表明，根据气象数据，可以预测这种电阻率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号