Optimizing Resource Allocation in Smart Cities for Sustainability Using Deep Reinforcement Learning and Advanced Econometric Models
DOI:
https://doi.org/10.51976/5k3ey911Keywords:
Smart cities, sustainability, deep reinforcement learning, econometrics, resource allocation, machine learning, Granger causality, VAR, Bayesian networksAbstract
By improving resource management, lowering environmental impact, and improving resident quality of living, smart cities using IoT, artificial intelligence, and big data are becoming more significant part of sustainable urban development. Particularly in energy usage, trash management, and water use, optimal resource allocation is a major obstacle these communities are confronting. This study presents a fresh strategy aiming at optimizing the resource allocation in the smart city by means of the synergy between the advanced economic models and DRL. Six statistical tools comprising Granger Causality, Co-integration Analysis, Dynamic Time Warping (DTW), Vector Autoregressive Models (VAR), Structural Equation Modelling (SEM), and Bayesian Networks have been applied to identify and examine the intricate interactions among several smart city components. The findings will provide a thorough awareness of how these instruments may enhance decision-making procedures so that municipal resources might be managed more ecologically and efficiently.
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