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Leak Detection using Random Forest and Pressure Simulation

机译:使用随机森林和压力模拟进行泄漏检测

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Water is a scarce resource which is becoming increasingly inaccessible. It is therefore necessary, in most parts of the world, to capture, transport and allocate it efficiently and thoughtfully. The implementation of monitored water distribution networks is often expensive. The purpose of this project is therefore to monitor leakage and consumption in a non-pressurized agricultural irrigation system using only inexpensive and easily installed pressure sensors. We modeled the water network to automatically simulate a leak randomly through the network. These simulated pressures serve as a dataset to train, test and validate a Random Forest algorithm that detects the leaks. Through pressure measures, the model can locate the junction closest to the leak with an accuracy of 96.24%. This approach therefore allows leaks detection in a water distribution system without the use of expensive flow sensors.
机译:水是一种稀缺资源,越来越难以获得。因此,在世界大部分地区,有必要高效,有思想地捕获,运输和分配它。受监控的供水网络的实施通常很昂贵。因此,该项目的目的是仅使用便宜且易于安装的压力传感器来监控非加压农业灌溉系统中的泄漏和消耗。我们对供水网络进行了建模,以自动模拟通过供水网络进行的随机泄漏。这些模拟压力用作训练,测试和验证检测泄漏的随机森林算法的数据集。通过压力测量,该模型可以以96.24%的精度定位最靠近泄漏的连接点。因此,该方法无需使用昂贵的流量传感器即可在配水系统中进行泄漏检测。

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