# Deep Learning-driven Community Resilience Rating based on Intertwined Socio-Technical Systems Features

arXiv: https://arxiv.org/abs/2311.01661 · HTML: https://mlsanity.com/paper/2311.01661/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 81 references that are in the map.
- First posted on arXiv 3 Nov 2023.

## Builds on (most cited first, 8 of 8)

- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [EXPLAIN-IT: Towards Explainable AI for Unsupervised Network Traffic Analysis](https://mlsanity.com/paper/2003.01670/) (2020)
- [Unsupervised Graph Deep Learning Reveals Emergent Flood Risk Profile of Urban Areas](https://mlsanity.com/paper/2309.14610/) (2023)
- [Collision of Environmental Injustice and Sea Level Rise: Assessment of Risk Inequality in Flood-induced Pollutant Dispersion from Toxic Sites in Texas](https://mlsanity.com/paper/2301.00312/) (2023)
- [Latent Sub-structural Resilience Mechanisms in Temporal Human Mobility Networks during Urban Flooding](https://mlsanity.com/paper/2203.16786/) (2022)
- [Quantitative Measures for Integrating Resilience into Transportation Planning Practice: Study in Texas](https://mlsanity.com/paper/2205.02758/) (2022)
- [Revealing Critical Characteristics of Mobility Patterns in New York City during the Onset of COVID-19 Pandemic](https://mlsanity.com/paper/2102.01918/) (2021)
- [Sustainable development-oriented campus bike-sharing site evaluation model: A case study of Henan Polytechnic University](https://mlsanity.com/paper/2308.04454/) (2023)

## Built on by (most cited first, 4 of 4)

- [CrisisSense-LLM: Instruction Fine-Tuned Large Language Model for Multi-label Social Media Text Classification in Disaster Informatics](https://mlsanity.com/paper/2406.15477/) (2024)
- [Can LLMs Understand Social Norms in Autonomous Driving Games?](https://mlsanity.com/paper/2408.12680/) (2024)
- [FloodDamageCast: Building Flood Damage Nowcasting with Machine Learning and Data Augmentation](https://mlsanity.com/paper/2405.14232/) (2024)
- [Data-efficient flood depth prediction through domain-aware coreset selection and tabular foundation models](https://mlsanity.com/paper/2606.05265/) (2026)

## Abstract (arXiv)

Community resilience is a complex and muti-faceted phenomenon that emerges from complex and nonlinear interactions among different socio-technical systems and their resilience properties. However, present studies on community resilience focus primarily on vulnerability assessment and utilize index-based approaches, with limited ability to capture heterogeneous features within community socio-technical systems and their nonlinear interactions in shaping robustness, redundancy, and resourcefulness components of resilience. To address this gap, this paper presents an integrated three-layer deep learning model for community resilience rating (called Resili-Net). Twelve measurable resilience features are specified and computed within community socio-technical systems (i.e., facilities, infrastructures, and society) related to three resilience components of robustness, redundancy, and resourcefulness. Using publicly accessible data from multiple metropolitan statistical areas in the United States, Resili-Net characterizes the resilience levels of spatial areas into five distinct levels. The interpretability of the model outcomes enables feature analysis for specifying the determinants of resilience in areas within each resilience level, allowing for the identification of specific resilience enhancement strategies. Changes in community resilience profiles under urban development patterns are further examined by changing the value of related socio-technical systems features. Accordingly, the outcomes provide novel perspectives for community resilience assessment by harnessing machine intelligence and heterogeneous urban big data.