Applying Machine Learning Tools for Urban Resilience Against Floods

TL;DR

This paper enhances flood resilience modeling in Tehran's District 6 by integrating machine learning with the Climate Disaster Resilience Index, creating a dynamic, data-driven tool for urban flood resilience planning.

Contribution

It introduces a novel integration of machine learning techniques with the CDRI model to enable temporal adaptability in urban flood resilience assessment.

Findings

The enhanced model predicts resilience dimensions for 2025.

Machine learning improves the model's temporal responsiveness.

The approach offers actionable insights for urban planners.

Abstract

Floods are among the most prevalent and destructive natural disasters, often leading to severe social and economic impacts in urban areas due to the high concentration of assets and population density. In Iran, particularly in Tehran, recurring flood events underscore the urgent need for robust urban resilience strategies. This paper explores flood resilience models to identify the most effective approach for District 6 in Tehran. Through an extensive literature review, various resilience models were analyzed, with the Climate Disaster Resilience Index (CDRI) emerging as the most suitable model for this district due to its comprehensive resilience dimensions: Physical, Social, Economic, Organizational, and Natural Health resilience. Although the CDRI model provides a structured approach to resilience measurement, it remains a static model focused on spatial characteristics and lacks temporal adaptability. An extensive literature review enhances the CDRI model by integrating data from 2013 to 2022 in three-year intervals and applying machine learning techniques to predict resilience dimensions for 2025. This integration enables a dynamic resilience model that can accommodate temporal changes, providing a more adaptable and data driven foundation for urban flood resilience planning. By employing artificial intelligence to reflect evolving urban conditions, this model offers valuable insights for policymakers and urban planners to enhance flood resilience in Tehrans critical District 6.