Machine Learning Paradigms For Deterioration Modeling Of Water Distribution Infrastructures Under Climatic And Environmental Conditions

"Migration to urban areas is expected to approach 68% of the world population in 2050, according to UN estimates (Nations, 2018). Maintaining sustainable water distribution networks is imperative for transporting clean water to consumers, thereby ensuring public health. In addition, water distribution networks are essential infrastructures worldwide. Their structural safety is critical to ensure that treated water does not leak into the ground, wasting millions of tax dollars. Understanding the factors that affect the operational performance of a given water distribution system can help prioritize maintenance and predict the approximate service life of the pipelines such that replacement can be appropriately planned. Artificial intelligence (AI) for modeling and predicting the failure of water pipes has become advantageous in recent years. AI and machine learning approaches are fundamental, predictive models that help decision-makers develop strategies that mitigate the risk of failure by labeling pipes requiring immediate repair within a water distribution network. However, the failure process of water distribution pipelines remains ambiguous, and it may occur for ''unknown reasons.''The intellectual contribution of this dissertation is to bridge the gap in the theoretical knowledge between critical factors and the deterioration of water distribution infrastructure. This dissertation also proposes new machine learning paradigms based on ensemble and deep learning to predict the failure of water distribution pipelines under various environmental and climatic conditions. To achieve the objectives of this dissertation, pipe failure data are collected from two municipalities in Canada, the City of London and Sainte-Foy in London and Quebec, respectively. In addition, climate data are amassed from the Environment and Climate Change Canada (ECCC) for the cities mentioned above. This dissertation research uncovers the effects of essential factors affecting the failure prediction of water pipelines. Of these essential factors, important ones to note are air temperature, minimum antecedent precipitation index, and evaporation. The results demonstrate that the failure process depends mainly on the climate conditions of the geographical location of water pipes. Furthermore, the results prove that the proposed approaches can leverage insightful knowledge even with limited exposure to training tasks. The results also demonstrate that the proposed approaches are flexible to limited, high-dimensional, and partially observed data. Moreover, the results show that these prediction methods can complement other statistical and state-of-the-art machine learning models. Lastly, the results validate the potential implementations of the proposed models for decision-making in water distribution networks"--