Company or Institution
Sustainable Development Goals (SDGs)
SDG 11: Sustainable Cities and Communities
SDG 13: Climate Action
General description of the AI solution
Resilitix’s innovation will transform the way communities prepare, respond, and recover from climate hazards by applying the Resilitix Digital Twin to their specific situations. Resilitix Digital Twin is an AI-enabled information system with three main modules: (1) infrastructure networks vulnerability analysis module, (2) critical facility access loss module, and (3) situational awareness module. Each module includes novel machine learning and deep learning models built on heterogeneous community datasets. With a focus on transportation infrastructure and critical facilities (e.g., hospitals), the hazard mitigation and risk reduction module will create network analytics models based on a fusion of features related to hazard exposure of infrastructure networks, co-location dependencies, network topology to determine the criticality of infrastructure and its vulnerability to floods. The critical facility access module will create a novel reinforcement learning model for classifying the extent of risk of disrupted access to critical facilities such as hospitals and dialysis centers. The situational awareness module will create spatial machine learning models through the use of location-intelligence and social media data to proactively and predictively monitor community response and recovery trajectories of spatial areas (e.g., census tracts).
Yuan, F., Xu, Y., Li*, Q., and Mostafavi, A. (2022). “Spatio-Temporal Graph Convolutional Networks for Road Network Inundation Status Prediction during Urban Flooding,” Computers, Environment and Urban Systems in April 2021, DOI: 10.1016/j.compenvurbsys.2022.101870.
Yuan, F., Fan, C., Farahmand*, H., Coleman, N., Esmalian, A., Lee, C., Patrascu, F., Zhang, C., Dong, S., Mostafavi, A. (2022). “Smart Flood Resilience: Harnessing Community-Scale Big Data for Predictive Flood Risk Monitoring, Rapid Impact Assessment, and Situational Awareness,” Environmental Research: Infrastructure and Sustainability, DOI: 10.1088/2634-4505/ac7251.
Fan, C., Jiang, X., and Mostafavi, A. (2021). “Adaptive Reinforcement Learning Model for Simulation of Urban Mobility during Crises,” Sustainable Cities and Society, DOI: 10.1016/j.scs.2021.103367.
Fan, C., Zhang, C., Yahja, A., and Mostafavi, A. (2019). “Disaster City Digital Twin: A Vision for Integrating Artificial and Human Intelligence for Disaster Management.” International Journal of Information Management, https://doi.org/10.1016/j.ijinfomgt.2019.102049