Insufficient public and municipal investment represent a major challenge for the long term management of urban drainage systems. Utilities are challenged to develop efficient rehabilitation strategies in order to maintain the level of service. Closed-circuit television (CCTV) inspection is used since the 1980’s as industry standard for sewer investigation system and structural performance evaluation. Due to budget restrictions, inspection rates are generally low and municipalities tend to inspect only a small part of their network (e.g. in France, less than 5% according to Ahmadi et al., 2014c). Since the definition of rehabilitation strategies is limited by the lack of information about sewer condition and remaining life, deterioration models have been developed to forecast the evolution of the system according to its current and past condition. One of the main factors hampering the uptake of deterioration modelling by utilities is the lack of real scale evidence of the tangible benefits provided. In particular, most utilities are concerned by the minimum amount of CCTV data required and the relevance of using such models on their networks with limited data availability. Finally, most utilities acknowledge the uncertainties in the procedure of sewer condition assessment, mainly due to the subjectivity of the coding operator. There is a strong need to quantify precisely the uncertainty of the sewer condition assessment procedure and its influence on the outcomes of deterioration modelling. The thesis aims at addressing these gaps by assessing the performance of sewer deterioration modelling using a case study with high CCTV data availability and by identifying the influence of CCTV data quality and availability on modelling performance. The study has been performed with a statistical (GompitZ) and a machine learning (Random Forest) deterioration models using the extensive CCTV database of the cities of Braunschweig and Berlin in Germany. Our results show, that at network level, both machine learning and statistical models can simulate with sufficient accuracy the condition distribution of the network, even in case of low data availability. At the pipe level, the machine learning model outperforms the statistical model. Regarding CCTV data uncertainty, our results highlight that the probability to inspect correctly a pipe in poor condition is close to 80-85% and thus the probability to overestimate the (good) condition of the pipe is close to 15-20% (False Negative). The impact of the uncertainties on the prediction of a deterioration model is not negligible. The analysis shows that the required replacement rate to maintain a constant proportion of segments in poor condition is underestimated if the uncertainties are not included in the analysis.