Using machine learning techniques to predict the cost of repairing hard failures in underground fiber optics networks

Abstract

AbstractFiber 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.