A Machine Learning Approach to Predict Site Selection from the Perspective of Vitality Improvement

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Article number2113
Journal / PublicationLand
Volume13
Issue number12
Online published6 Dec 2024
Publication statusPublished - Dec 2024

Link(s)

Abstract

The selection of construction sites for Cultural and Museum Public Buildings (CMPBs) has a profound impact on their future operations and development. To enhance site selection and planning efficiency, we developed a predictive model integrating Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs). Taking Shanghai as our case study, we utilized over 1.5 million points of interest data from Amap Visiting Vitality Values (VVVs) from Dianping and Shanghai’s administrative area map. We analyzed and compiled data for 344 sites, each containing 39 infrastructure data sets and one visit vitality data set for the ANN model input. The model was then tested with untrained data to predict VVVs based on the 39 input data sets. We conducted a multi-precision analysis to simulate various scenarios, assessing the model’s applicability at different scales. Combining GA with our approach, we predicted vitality improvements. This method and model can significantly contribute to the early planning, design, development, and operational management of CMPBs in the future. © 2024 by the authors.

Research Area(s)

  • cultural and museum public buildings, building site selection, development vitality, artificial neural network, genetic algorithm

Citation Format(s)

A Machine Learning Approach to Predict Site Selection from the Perspective of Vitality Improvement. / Zhao, Bin; Zheng, Hao; Cheng, Xuesong.
In: Land, Vol. 13, No. 12, 2113, 12.2024.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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