Smart housing: integrating machine learning in sustainable urban planning, interior design, and development

Abstract

Smart housing, therefore, theoretically becomes very vital in this context of a smart city for sustainable urban planning and development. Machine learning technologies can be considered quite fundamental in enhancing efficiency, sustainability, and livability through incorporating into smart housing. However, rapid urbanization, population growth, traffic congestion, and energy management are huge problems. The main objective of this research work is to identify the feasibility of ML application in smart housing for resource management optimization, environmental sustainability, and public safety. It conducts an analysis on key factors like energy consumption, waste management, and public safety measures by applying machine learning’s efficient algorithms on the comprehensive dataset. There is a 20% decrease in total energy consumption, 15% increase in renewable source energy consumption, and a 25% efficiency improvement in waste management. In addition, public safety response times decreased by 30%. Also, ML models gave out very accurate predictions for power use, traffic patterns, and air quality that turned out with an average accuracy of 92%, thus saving 10% carbon emissions. The study clearly showed that ML will play a very key role in housing planning and interior design. The results bring out the importance of ML in tackling challenging urban issues and promoting better sustainable urban planning practices.