{"id":11750,"date":"2025-05-12T17:34:23","date_gmt":"2025-05-12T08:34:23","guid":{"rendered":"https:\/\/aiesg.co.jp\/?post_type=news&p=11750"},"modified":"2025-05-12T17:34:23","modified_gmt":"2025-05-12T08:34:23","slug":"250512-city-level-population-prediction-in-japan-from-2020-to-2100-by-machine-learning","status":"publish","type":"news","link":"https:\/\/aiesg.co.jp\/en\/topics\/news\/250512-city-level-population-prediction-in-japan-from-2020-to-2100-by-machine-learning\/","title":{"rendered":"aiESG Chief Data Scientist Li is the first author of the paper \"City-level population prediction in Japan from 2020 to 2100 by machine learning\" published in Expert System with Application Application\" is published in the journal Expert System with Application."},"content":{"rendered":"

aiESG Chief Data Scientist Li is the first author of the paper \"City-level population prediction in Japan from 2020 to 2100 by machine learning\" published in the journal Expert System with Application\".

This study uses machine learning to forecast Japan's population by city to the year 2100. As Japan's population continues to age, detailed city-level projections are essential for urban planning and policy making. The results suggest that major cities such as Tokyo and Osaka are relatively resilient to aging, while remote rural areas are more susceptible. This suggests the need for policies tailored to the characteristics of each region.

This research is expected to provide highly accurate future predictions and new analytical methods that will contribute to solving the problems of an aging society.

Paper Title:.<\/strong>
City-level population prediction in Japan from 2020 to 2100 by machine learning<\/a>
Author(s).<\/strong>\u00a0
Chao Li, Alexander Ryota Keeley, Shutaro Takeda, Daikichi Seki, Jiaxu Zhang, Bo Shi, Shunsuke Managi\u00a0

RELATED:
A paper on the joint research conducted by aiESG President Managi and Shimamura with Tanaka has been published.<\/a>

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