Technical and economic impact of the huge internet of things and artificial intelligence in the creation of the intelligent water network

Promoting health and prosperity in all aspects of daily life is a challenge that every Man and woman is called to embrace. As an example, pursuing water safety is essential for maintaining municipal infrastructure and public health, and improving the quality of people's lives. This thesis proposes a computational risk assessment methodology involving the use of different Artificial Intelligence (AI) techniques to guarantee, above all, three main concerns: interpretability, data scarcity, and performance. To this aim, a methodology based on the use of AI techniques, Fuzzy Logic (FL), and Machine Learning (ML) is proposed here. To make this methodology concrete, based on the Water domain. Water quality assessment is the first study domain, developed in both Understanding Water Distribution Networks risk assessment, as well as understanding the main factors affecting the potability of water. The first aspect is dealt with a computational Risk assessment methodology involving the use of Fuzzy Inference System (FIS) with Monte Carlo Simulation (MCS) to quantify and prioritize operational, environmental, and structural risks in Water Distribution Network (WDN). The methodology enhances traditional Failures Modes and Effects Analysis (FMEA) by taking linguistic Imprecision in the judgment of the expert into account, replacing deterministic Risk Priority Number (RPN) with fuzzy-based risk assessment. In addition, a ML pipeline is built based on data to make predictions about water potability in terms of physicochemical Features. Model performances are evaluated by cross-validation, Receiver Operating Characteristic Curve (ROC) curves, and interpretability metrics like permutation importance and SHapley Additive exPlanations (SHAP) values to determine the most significant factors affecting water quality and pH, sulfate, and Total Dissolved Solids (TDS) were the most reliable parameters for water potability. In this thesis, we also implemented ML models, including advanced ensemble methods and a stacking meta-model, for the prediction of water stress and classification of water scarcity on a Global scale. This thesis evaluates the technical and economic performance of Intelligent Water Networks (IWNs) by integrating Internet Of Things (IOT) sensors and AI Techniques. Various AI approaches, including ML models and rule-based methods such as as FIS, were applied to monitor water quality and network parameters. Performance metrics such as accuracy, precision, recall, and F1 score were analyzed, and model interpretability was employed by using tools like SHAP to provide more explanations for Decision-making and network optimization.