Enhancing preservation outcomes for architectural heritage buildings through machine learning-driven future search optimization

Abstract

Architectural heritage represents an invaluable tapestry of human civilization’s artistic, cultural, and historical milestones. As urbanization and societal dynamics evolve, preserving these architectural marvels becomes increasingly imperative. Drawing upon a meticulously curated dataset of UNESCO World Heritage Sites, we introduce a novel machine-learning model that harnesses the capabilities of XGBoost in conjunction with Future Search Optimization (FSO). Our findings reveal that the XGBoost-FSO hybrid accurately identifies and categorizes at-risk heritage sites. From an architectural preservation perspective, the transformative potential of such predictive analytics is immense. They enable timely interventions, ensuring that heritage structures, each with its unique narrative and significance, endure the tests of time. By bridging the realms of technology and architectural heritage, this research underscores the promise of data-driven strategies in championing the cause of global heritage preservation. Through this interdisciplinary approach, we envision a future where technology acts as the vanguard, safeguarding the architectural legacies of yesteryears for generations to come.