A Hybridized Machine Learning and Optimization Paradigm for Multi-Objective, Context-Aware Disaster Recovery Planning

Arun K Gangula

doi:10.36948/ijfmr.2023.v05i05.49000

A Hybridized Machine Learning and Optimization Paradigm for Multi-Objective, Context-Aware Disaster Recovery Planning

Author(s)	Arun K Gangula
Country	United States
Abstract	Disaster recovery planning (DRP) encounters growing obstacles because natural and human-made catastrophic events occur more frequently and become more complex while producing greater impacts. The dynamic characteristics of disaster environments, along with multiple conflicting objectives and large volumes of crisis-generated data, render traditional disaster recovery planning (DRP) approaches ineffective. The proposed research introduces a new conceptual framework that combines Machine Learning (ML) with Optimization methods inside a context-sensitive system to tackle current challenges. The proposed paradigm utilizes machine learning (ML) for predictive analytics, situational awareness, and damage assessment through real-time analysis of contextual data. The optimization models receive guidance from ML-derived insights to optimize resource distribution, recovery task scheduling, and logistics management with equal efficiency. The system depends on context-awareness to modify its operations based on information from IoT sensors, GIS, and social media during evolving disaster situations. The paradigm operates through a feedback loop that combines action results with contextual data to enhance both machine learning models and optimization parameters, while enabling adaptive learning and decision refinement. The proposed system demonstrates its potential usage in various disaster scenarios, including urban flooding, earthquakes, and wildfires. The implementation of this paradigm faces several obstacles, which include data collection and maintenance standards, as well as algorithmic expansion, model validation, and ethical concerns. The paper explains the proposed paradigm’s architecture and components, along with their interactions, while demonstrating its potential to enhance disaster recovery effectiveness and community resilience and identifying essential research directions.
Field	Engineering
Published In	Volume 5, Issue 5, September-October 2023
Published On	2023-10-06
DOI	https://doi.org/10.36948/ijfmr.2023.v05i05.49000
Short DOI	https://doi.org/g9q3z4

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A Hybridized Machine Learning and Optimization Paradigm for Multi-Objective, Context-Aware Disaster Recovery Planning

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