EventsMOL2NET'20, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 6th ed.
Event submissions
Published
with-doi10.3390/mol2net-06-06951 (registering DOI)
This submission belongs to the session 07. AIMEDIC-07: Int. Congress in Med. Info., UDC, Coruña, Spain-Sandford, USA, 2019 of the event MOL2NET'20, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 6th ed.
Published date
18 Oct, 2020
Citation
Adrián Carballal, Rafael Iván Pazos Pérez, Alvaro Torrente-Patiño, Nereida Rodriguez-Fernandez, Predicting Vertical Urban Growth Using Genetic Evolutionary Algorithms in Tokyo’s Minato Ward, in Proceedings of MOL2NET'20, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 6th ed., 30 January 2020–30 January 2021, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-06-06951
Share
Email
Facebook
Twitter
LinkedIn

Predicting Vertical Urban Growth Using Genetic Evolutionary Algorithms in Tokyo’s Minato Ward

image
Adrián Carballal 1,2
Rafael Iván Pazos Pérez 2
Alvaro Torrente-Patiño 1,2
image
Nereida Rodriguez-Fernandez 1,2
1. RNASA-IMEDIR
2. UDC
Abstract

This research explores the use of artificial intelligence to simulate how cities will grow
vertically. By learning how cities have evolved in the recent past, genetic algorithms can
successfully simulate vertical urban growth. The research was applied to buildings 130 meters
and taller in the Minato Ward of Tokyo in 2015. An evolutionary computer model was built
from a standard genetic algorithm, using historical and economic data, which then simulated
future growth for the 2016 to 2019 period. The results obtained matched the area of study’s
real vertical growth for the study period, with a 85.7% accuracy for the number of buildings,
73.7% for their average heights, and 96.3% for the likelihood of new construction projects
happening within a mapped area. By learning how a city evolved in the past, the model
replicated the future vertical growth of a city center.

Keywords
artificial intelligence
urban growth
genetic evolutionary algorithms
Manuscript
urban_growth_tokyo.pdf
Generación de modelos predictivos de péptidos anti-angiogénicos para el proyecto CICLOGEN
Inteligencia artificial para el movimiento de barcos en puerto y condiciones de emergencia