IMPACT ASSESSMENT OF MACHINE LEARNING ALGORITHMS ON RESOURCE EFFICIENCY AND MANAGEMENT IN URBAN DEVELOPMENTS

Abstract

Urban centers face the mounting challenge of balancing resource demands with sustainable practices in the face of population growth and environmental concerns. Machine learning (ML) has emerged as a transformative technology with the potential to optimize resource efficiency and management within urban environments.  This article investigates the multifaceted impact of ML algorithms on enhancing resource management and the associated challenges and considerations. It delves into successful ML applications in vital urban sectors, including smart grids, water conservation, and intelligent transportation systems. Through the analysis of case studies, the article quantifies improvements in resource efficiency and highlights the contributions of ML to data-driven decision-making.  Crucially, it emphasizes the need for a holistic approach, addressing computational costs, data bias, privacy concerns, and ethical considerations to ensure the responsible and equitable deployment of ML. The article concludes by underscoring the ongoing evolution of ML and its pivotal role in shaping sustainable and resilient urban futures.