Artificial Intelligence and Machine Learning for Future Urban Development

Abstract

Artificial intelligence methods are extensively utilized greatly as substitutes rather than more classical methods for modeling the environmental frameworks. In this chapter there will be a review of some, and how they are applied in the environment, including examples and concrete references are provided. Moreover, the methods will be case focused reasoning, fuzzy models, rule based frameworks, artificial networks, cellular automata, the swarm intelligence, multi-agents frameworks, reinforcement learning systems, and the hybrid systems. Therefore, city designers and architects allocate much time in gathering data from crucial sources. Increased augmenting of the GIS (Growth information systems) have made is effective in mapping and portraying the data collected in the laboratory, although such tools are restricted due to insufficient of complex data and inference capacities. Moreover, this chapter will state the probable chances of how artificial intelligence and the machine based learning techniques may improve the performance processes of urban planning through the provision of extensive data evaluation and inference capacities. Verifying the claim, some of the machine learning techniques have been utilized which clearly point out the type of urban settings and major streets grouping, centered on how complex semantic and spatial relationships unlike the building geometry.