Assessment of water resources system resilience under hazardous events using system dynamic approach and artificial neural networks

Abstract

The objective of this research is to propose a novel framework for assessing the consequences of hazardous events on a water resources system using dynamic resilience. Two types of hazardous events were considered: a severe flood event and an earthquake. Given that one or both hazards have occurred and considering the intensity of those events, the main characteristics of flood dynamic resilience were evaluated. The framework utilizes an artificial neural network (ANN) to estimate dynamic resilience. The ANN was trained using a large, generated dataset that included a wide range of situations, from relatively mild hazards to severe ones. A case study was performed on the Pirot water system (Serbia). Dynamic resilience was derived from the developed system dynamics model alongside the hazardous models implemented. The most extreme hazard combination results in the robustness of 0.04, indicating a combination of an earthquake with a significant magnitude and a flood hydrograph with a low frequency of occurrence. In the case of moderate hazards, the system robustness has a median value of 0.2 and a rapidity median value of 162 h. The ANN's efficacy was quantified using the average relative error metric which equals 2.14% and 1.77% for robustness and rapidity, respectively. HIGHLIGHTS Proposition of a generalized methodological framework to quantify flood dynamic resilience of water systems using a significant amount of generated data related to multiple hazards.; Implementation of the results derived from the dam safety model for accurate estimation of the earthquakes’ impacts.; Application of ANN to predict the main characteristics of the flood-risk related matrices.;

Keywords

• flood dynamic resilience
• flood risk assessment
• machine learning
• pirot water resources system
• system dynamics modeling approach