ReconAI: Data-driven infrastructure resilience assessment toward climate adaptation and conflict-resilience
Funding: British Academy Fellowship, Total funding: £100k
This research is in support of critical infrastructure recovery in war-torn countries, which are faced with adverse climatic and conflict-induced stressors. Ukraine is currently facing tremendous recovery challenges and will require immense efforts to recover destroyed critical infrastructure, such as roads, railways and energy assets. Also, the post-war recovery should optimise resilience whilst keeping carbon emissions at reasonable levels, which will be a great challenge for the infrastructure sector. Decision-making towards financing post-conflict reconstruction and asset management requires smart data, systemic approaches and meaningful metrics, to quantify the trade-offs and synergies between climate-resilience, conflict-resilience and sustainability in infrastructure reconstruction and adaptation, which is the main objective of this project.
The framework will combine Big Data pre-processing techniques, the use of genetic algorithms (GA), novel machine learning (ML) ensembles and continuous learning models and remote sensing data (e.g. satellite imagery, macrophotos). The research will set the bedrock for a holistic, systemic, data-driven infrastructure resilience assessment toward climate adaptation and conflict-resilience in Ukraine, aiming to inform investors for the financial feasibility of reconstruction strategies.