Fiber optics cable has been adopted by telecommunication companies worldwide as the primary medium of transmission. The cable is steadily replacing long-haul microwave, copper cable, and satellite transmissions systems. Fiber cable has been deployed in an underground, submarine, and aerial architecture to transmit high-speed signals in intercontinental, inter countries, inter cities and intra-cities. Underground fiber cable transmission has experienced major failures as compared to other mediums of fiber transmission infrastructure. The failure is rampant, and especially the cable get cuts frequently in areas where there are road constructions, road road expansion projects, and other developmental projects. The cost of repairing these failures is enormous, and it largely depends on the cause of failure and the geographical area the faults occurred. The main aim of this paper was to investigate the cost of repairing underground fiber cable failures, clustered the cause of faults, and then used feedforward neural networks (FFNN) and linear regression to predict the cost of repairing future faults. The result of the predictive model is significant to the telecommunications industry, which means the cost of repairing an underground optical networks will be known to the industry players before the fault occurs. depending on which area, the cause of the failure and the mean time to repair (MTTR), the predictive model tells the mobile network operators the cost involved to repair the damaged cable. The accuracy of the result obtained indicates the predictive model is good for predicting the cost of repairing fiber cable cut in underground optical networks.

中文翻译：

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使用机器学习技术预测修复地下光纤网络中的硬故障的成本

光纤电缆已被全球各地的电信公司用作主要传输介质。该电缆正在稳步取代长距离微波，铜缆和卫星传输系统。光缆已被部署在地下，海底和空中建筑中，以在洲际，国家间，城市间和城市内传输高速信号。与其他光纤传输基础架构介质相比，地下光纤电缆传输经历了重大故障。破坏非常严重，尤其是在有道路建设，道路扩建项目和其他开发项目的地区，电缆经常被切断。修复这些故障的成本是巨大的，并且在很大程度上取决于故障原因和故障发生的地理区域。本文的主要目的是研究修复地下光缆故障的成本，对故障原因进行聚类，然后使用前馈神经网络（FFNN）和线性回归来预测修复未来故障的成本。预测模型的结果对电信行业而言意义重大，这意味着在故障发生之前，行业参与者将知道修复地下光网络的成本。根据哪个区域，故障原因和平均修复时间（MTTR），该预测模型会告诉移动网络运营商修复受损电缆的成本。所获得结果的准确性表明，该预测模型可很好地预测地下光网络中修复的光缆切割成本。