• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Energy, Communication, Data Analytics and Soft Computing >Leakage detection and prediction of location in a smart water grid using SVM classification
【24h】

Leakage detection and prediction of location in a smart water grid using SVM classification

机译：使用SVM分类的智能水网中的泄漏检测和位置预测

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

摘要

A smart water grid should be capable of ensuring the 24*7 reliable water supplies with efficient distribution of water with minimum losses due to leakages in pipelines. Early Detection of water leakages is utmost important to save large amount of water from being wasted. This paper presents the SVM classification technique for detection of leakages and prediction of location in a water network. Besides the traditional methods of identifying a leakage which incurs a high cost but having a low efficiency, SVM classification technique can be used to classify the leakage and non-leakage scenarios using the pressure and flow dataset captured from the water distribution network. The dataset is obtained by simulating the water distribution network of CSIR-CEERI, Pilani into EPANET tool.
机译：智能水网应能够确保24 * 7的可靠供水，并能有效分配水，并且将因管道泄漏而造成的损失降至最低。尽早发现漏水对于避免浪费大量水至关重要。本文介绍了用于水网渗漏检测和位置预测的SVM分类技术。除了成本高，效率低的传统识别泄漏方法外，SVM分类技术还可用于使用从配水管网捕获的压力和流量数据集对泄漏和非泄漏情景进行分类。通过将CSIR-CEERI，Pilani的水分配网络模拟为EPANET工具来获得数据集。

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