Cognitive AI Models for Predictive Urban Sustainability: A Hybrid Deep Learning Framework

Abstract

The rise in the amount of population, energy consumption, and climate issues have been issues of concern worldwide, which have given rise to urban sustainability. In the paper, the author explains about a hybrid deep learning system that integrates Cognitive Artificial Intelligence (AI) with urban analytics to predict the sustainability outcomes of the main areas, such as energy efficiency, waste management, transportation, and air quality. The proposed model will make use of Convolutional Neural Network (CNN) to compute the spatial information and Recurrent Neural Network (RNN) to compute the trends in order to realize real-time adaptive predictions. The open urban data was subjected to experimental validation, which demonstrated that the predictive accuracy of the open urban data is 91 percent and the wastefulness of the resource is minimized. It is an AI cognitive model that will help policymakers and urban planners to plan a sustainable city development using data.