| Abstract: This research aims to propose a novel framework for the assessment of the consequences of hazardous events on a water resources system using dynamic resilience. The two main types of hazardous events considered are: a severe flood event, and an earthquake. Given that one or both hazards occur, this framework utilizes a digital twin based on a system dynamics (SD) model, backed by an Artificial Neural Network (ANN) to estimate the dynamic resilience. The ANN was trained using a large, simulated dataset ranging from very mild to extreme hazard combinations. The ANN’s efficacy was quantified using the average relative error metric which equals 2.14% and 1.77% for robustness and rapidity, respectively. |