1. Introduction

This package was developed as part of the Disaster REsilience Assessment, Modelling, and INno-vation Singapore (DREAMIN’SG) project at the Future Resilient Systems of the Singapore-ETH Centre. The DREAMIN’SG project is funded by the National Research Foundation, Singapore under the Intra-CREATE grant program. In the wake of increasing threats posed by climate change on urban infrastructure [1], this project aims at studying the effects of policy interventions and network characteristics on the resilience of urban infrastructure networks. Given the critical nature of the above infrastructure systems, their interdependencies need to be considered in pre- and post-disaster resilience actions.

In specific, the DREAMIN’SG project envisaged to build a platform to assess and predict the resilience of urban infrastructure systems and propose new pathways to develop innovative technologies and services for its improvement. The urban infrastructure system is modeled as an interdependent power-, water-, and transportation network that interact with each other before, during and after a disaster. The researchers are developing an integrated simulation model to study the performance of the interdependent infrastructure network under various disruption and recov-ery scenarios. Based on the simulation-generated datasets, machine learning algorithms are implemented to understand the causal relationship between topological and policy-related interventions and disaster risks. The results of the research are intended to support local governments and system managers to improve infrastructure resilience against weather-related disruptions. The methodology adopted in the study is presented in Fig. 1.1.

methodological framework

Fig. 1.1 Methodological framework of DREAMIN’SG project

The steps of the project are summarized as follows:

  1. Multiple scenarios are generated by considering different disruptions, network models, technological constraints and system configurations.

  2. A simulation model is created for the interdependent power grid, water distribution system, and road transportation system.

  3. Resilience is assessed based on the simulated performance of the three systems.

  4. An interpretable machine learning algorithm is implemented to analyze the scenarios and extract information related to key system features that influence resilience.

  5. The identified system features inform the design of new services, technologies, and prod-ucts that are able to simultaneously enhance resilience and accommodate the technological constraints.

For further information and updates on the project, please visit the DREAMIN’SG webpage. In the rest of the documentation, the details of the interdependent infrastructure simulation platform,including its modeling, installation, and usage are discussed.