| Abstract |
Highlights: What are the main findings? Ensemble learning methods proved superior in accurately predicting the depth of bearing layers critical to urban infrastructure, improving geotechnical predictions and urban planning. The integration of smart technologies and predictive analytics significantly improved the accuracy of geotechnical investigations, facilitating the development of smart cities by providing advanced tools for real-time data analysis and geographic mapping. What are the implications of the main finding? The success of ensemble learning in accurately predicting the depth of bearing layers can lead to more reliable urban planning and infrastructure development, reducing the risks associated with building on unstable ground. By using advanced predictive analytics, smart city technologies can significantly improve the resilience and sustainability of urban environments, leading to better preparedness for environmental challenges and disasters. This paper addresses challenges and solutions in urban development and infrastructure resilience, particularly in the context of Japan’s rapidly urbanizing landscape. It explores the integration of smart city concepts to combat land subsidence and liquefaction, phenomena highlighted by the 2011 Great East Japan Earthquake. Additionally, it examines the current situation and lack of geoinformation and communication technology in the concept of smart cities in Japan. Consequently, this study employs advanced technologies, including smart sensing and predictive analytics through kriging and ensemble learning, with the objective of enhancing the precision of geotechnical investigations and urban planning. By analyzing data in Setagaya, Tokyo, it develops predictive models to accurately determine the depth of bearing layers that are critical to urban infrastructure. The results demonstrate the superiority of ensemble learning in predicting the depth of bearing layers. Two methods have been developed to predict undetected geographic data and prepare ground reality and digital smart maps for the construction industry to build smart cities. This study is useful for real-time analysis of existing data, for the government to make new urban plans, for construction companies to conduct risk assessments before doing their jobs, and for individuals to obtain real-time geographic data and hazard warnings through mobile phones and other means in the future. To the best of our knowledge, this is the first instance of predictive analysis of geographic information being conducted through geographic information, big data technology, machine learning, integrated learning, and artificial intelligence. © 2024 by the authors. |
