A Machine Learning Approach to Predict Site Selection from the Perspective of Vitality Improvement
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 Algorith...
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MDPI AG
2024-12-01
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| author | Bin Zhao Hao Zheng Xuesong Cheng |
| author_facet | Bin Zhao Hao Zheng Xuesong Cheng |
| author_sort | Bin Zhao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3a7f1e2be01b49d68329ba1a31ba2e91 |
| institution | Kabale University |
| issn | 2073-445X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-3a7f1e2be01b49d68329ba1a31ba2e912024-12-27T14:35:10ZengMDPI AGLand2073-445X2024-12-011312211310.3390/land13122113A Machine Learning Approach to Predict Site Selection from the Perspective of Vitality ImprovementBin Zhao0Hao Zheng1Xuesong Cheng2Shanghai Academy of Fine Arts, Shanghai University, Shanghai 200444, ChinaArchitectural Intelligence Group, Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong SAR, ChinaShanghai Academy of Fine Arts, Shanghai University, Shanghai 200444, ChinaThe 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.https://www.mdpi.com/2073-445X/13/12/2113cultural and museum public buildingsbuilding site selectiondevelopment vitalityartificial neural networkgenetic algorithm |
| spellingShingle | Bin Zhao Hao Zheng Xuesong Cheng A Machine Learning Approach to Predict Site Selection from the Perspective of Vitality Improvement Land cultural and museum public buildings building site selection development vitality artificial neural network genetic algorithm |
| title | A Machine Learning Approach to Predict Site Selection from the Perspective of Vitality Improvement |
| title_full | A Machine Learning Approach to Predict Site Selection from the Perspective of Vitality Improvement |
| title_fullStr | A Machine Learning Approach to Predict Site Selection from the Perspective of Vitality Improvement |
| title_full_unstemmed | A Machine Learning Approach to Predict Site Selection from the Perspective of Vitality Improvement |
| title_short | A Machine Learning Approach to Predict Site Selection from the Perspective of Vitality Improvement |
| title_sort | machine learning approach to predict site selection from the perspective of vitality improvement |
| topic | cultural and museum public buildings building site selection development vitality artificial neural network genetic algorithm |
| url | https://www.mdpi.com/2073-445X/13/12/2113 |
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