Data-driven Approaches to Built Environment Flood Resilience: A Scientometric and Critical Review

Pavithra Rathnasiri*, Onaopepo Adeniyi, Niraj Thurairajah

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

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Environmental hazards such as floods significantly frustrate the functionality of built assets. In addressing flood-induced challenges, data usage has become important. Despite existing vast flood-related research, no research has presented a comprehensive insight into global studies on data-driven built environment flood resilience. Hence, this study conducted a comprehensive review of data-driven approaches to flood resilience. Scientometric analysis revealed emerging countries, authorships, keywords, and research hotspots. The critical review revealed data-centric approaches such as Machine Learning (ML), Artificial Intelligence (AI), Flood Simulations, Bayesian Modelling, Building Information Modelling (BIM) and Geographic Information Systems (GIS). However, they were mainly deployed in hydraulic flood simulations for prediction, monitoring, risk, and damage assessments. Further, the potentials of computational methods in tackling built environment resilience challenges were identified. Deploying the approaches in the future requires a better understanding of the status quo. These methods include hybrid data-driven approaches, ontology-based knowledge representation, multiscale modelling, knowledge graphs, blockchain technology, convolutional neural networks, automated approaches integrated with social media data, data assimilation, BIM models linked with sensors and satellite imagery and ML and AI-based digital twin models. Nevertheless, reference to data-informed built-asset resilience decisions and clear-cut implications on built-asset resilience improvement remain indistinct in many studies. This suggests that more opportunities exist to contextualise data for built environment flood resilience. This study concluded with a conceptual map of flood context, methodologies, data types engaged, and future computational methods with directions for future research.
Original languageEnglish
Article number102085
JournalAdvanced Engineering Informatics
Early online date7 Jul 2023
Publication statusPublished - 1 Aug 2023

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