A machine learning approach for improving monitoring in passive optical networks with almost equidistant branches is proposed and experimentally validated. It achieves a high diagnostic accuracy of 98.7% and an event localization error of 0.5m

翻译：提出并实验验证了一种基于机器学习的方法，用于改进具有近似等距分支的无源光网络监测。该方法实现了98.7%的高诊断准确率以及0.5米的事件定位误差。