Integrating Policy Instruments for Enhanced Urban Resilience: A Machine Learning and IoT-Based Approach to Flood Mitigation
Year: 2024
Type: article
Abstract: In the context of global urbanization, the interconnected architecture of economic, social, and administrative activities in modern cities cultivates a complex web of interdependencies. This intricacy amplifies the impacts of natural disasters such as urban flooding, presenting unprecedented challenges in risk management and disaster responsiveness. This paper advances the foundational theory of resilience and critiques the traditional vulnerability paradigm, advocating for a policy shift towards 'resilience governance'. By integrating structural and non-structural flood mitigation strategies at the onset of urban planning, the study emphasizes a holistic approach to enhancing urban flood resilience. Initially, the paper delineates the concept of urban resilience and outlines its practical applications in managing flood risks. Subsequently, it proposes an innovative framework that synergizes structural methodologies with advanced technologies like the Internet of Things (IoT) and non-structural decision-support tools powered by Machine Learning Algorithm. This integrated approach aims to bolster cities’ capacities for early flood warning, enable informed urban infrastructure planning, and mitigate flood-related risks. The findings contribute to the development of resilient urban governance models, highlighting the essential role of adaptive policy instruments in urban flood management.
Source: Water
DOI: 10.3390/w16233364
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,955
OpenAlex ID: W4404698733